PUBLICATION LIST OF MACHINE LEARNING AND INFERENCE

LABORATORY

1988-1997

MLI97-1

April 1m

bullA regularly updated version of the list and a description of research activities of the Machine Learning and Inference (MLl) Laboratory is on the Laboratorys web site httpwwwmligmuedu

For a list of earlier publications of the MLI Laboratory (1969-1987) or copies of any publications (1969-1997) contact Research Manager Machine Learning and Inference Laboratory Science and Technology BId n MS 4A5 George Mason University 4400 University Drive Fairfax VA 22030-4444 Phone 703-993-1719 Fax 703-993-3729 Email adminaicgmuedu

Publications of the Machine Learning and Inference Laboratory

George Mason University (1988 bull 1997)

1988 P88-l Michalski RS and Watanabe L Constructive Oosed-Loop ~Fundamental Ideas and Examples Reports of the Machine Learning and Inference ratory MLI 88-1 George Mason University Fairfax VA 1988

P88-2 Ko H and Michalski RS bull Types of Explanation and Their Role in Constructive Closedshyloop Learning Reports of the Machine Learning and Inference Uiboratory MLI 88-2 George Mason University Fairfax VA 1988

P88-3 Mozetic I and Lavrac N Incremental Learning from Examples in a Logic-Based Fonnalism Proceedings of Machine Learning Meta-Reasoning and Logic Workshop Sesimbra Portugal February 1988

P88-4 Michalski RS and Ko Ht On the Nature of Explanation or Why Did the Wine Bottle Shatter Proceedings ofthe Spring Symposium Series Explanation-Based Learning pp 12shy21 Stanford University March 1988

P88-5 CharIM Cherkassky V Wechsler H and Zimmennan GL Distributed and FaultshyTolerant Computation for Retrieval Tasks Using Distributed Associative Memories IEEE Transactions on Computers Vol 37 No4 pp 484-490 April 1988

P88-6 Ko H Empirical Assembly Planning A Learning Approach PhD Dissertation University of Illinois Urbana-Champaign May 1988

P88-7 Stepp R Whitehall BL and Holder LB Toward Intelligent Machine Learning Algorithms Reports of Coordinated Science Uiboratory UILU-ENG-88-2221 College of Engineering University of Illinois Urbana-Champaign May 1988

P88-8 Holder LB Discovering Substructure In Examples Reports of Coordinated Science Uiboratory UILU-ENG-88-2223 College of Engineering University of Illinois UrbanashyChampaign May 1988

P88-9 Stepp RE Machine Learning from Structured Objects Reports of Coordinated Science Laboratory pp 353-363 University of Illinois Urbana-Champaign May 1988

P88-10 Holder LB Substructure Discovery in SUBDUE Reports of Coordinated Science Uiboratory UILU-ENG-88-2220 College of Engineering University of Illinois UrbanashyChampaign May 1988

P88-11 Whitehall BL Substructure Discovery of Macro-Operators Reports 0 CoordinaJed Science Laboratory UlLU-ENG-88-2219 College of Engineering University of lllinois Urbana-Champaign May 1988

P88-12 Nowicki AR A Methodology for Representing Natural Language Expressions in VariableshyValued Logic Reports 0 the Machine Learning and Inference Laboratory MLI 88-3 George Mason University Fairfax V A June 1988

P88-13 Greene GH The Abacus2 System for Quantitative Discovery Using Dependencies to Discover Non-Linear Terms Reports 0 the Machine Learning and Inference Laboratory MLI 88-4 George Mason University Fairfax VA June 1988

P88-14 De Jong KA and Schultz AC Using Experience-Based Learning in Game Playing Proceedings 0 the Fifth InteTlUltional Conference on Machine Learning Ann Arbor MI Oxford Clarendon Press pp 284-290 June 1988

P88-1S Dontas K APPLAUSE An Implementation of the Collins-Michalski Theory of Plausible Reasoning MS Thesis Computer Science Departtnen~ The University of Tennessee Knoxville TN August 1988

P88-16 Bergadano F Matwin S Michalski RS and Zhang J A General Criterion for Measuring Quality ofConcept Descriptions Reports 0 the Machine Learning and Inference Laboratory MLI 88-S George Mason University Fairfax VA October 1988

P88-17 Bergadano F Matwin S Michalski RS and Zhang J Measuring Quality of Concept Descriptions Proceedings of the Third European Working Session on Learning Glasgow pp 1-14 October 1988

P88-18 Bergadano F Matwin S Michalski RS and Zhang J Representing and Acquiring Imprecise and Context-Dependent Concepts in Knowledge-based Systems Proceedings 0 the 3rd InteTlUltional Symposium on Methodologies for Intelligent Systems pp 270-280 Turin Italy October 1988

P88-19 Bergadano F Matwin S Michalski RS and Zhang Learning Two-Tiered Descriptions of Flexible Concepts A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part I Principles and Methodology Reports 0 the Machine Learning and Inerence Laboratory MLI 88-6 George Mason University Fairfax VA November 1988

P88-20 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of flexible Concepts A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part ll Algorithms and Experiments Reports ofthe Machine Learning and Inference Laboratory MLI 88-7 George Mason University Fairfax VA November 1988

P88-21 Collins A and Michalski RS The Logic of Plausible Reasoning A Core Theory Reports of the Machine Learning and Inference lAboratory MLI 88-8 George Mason University Fairfax VA November 1988

P88-22 Wechsler H and Zimmennan GL bull 2_D Invariant Object Recognition Using Disuibuted Associative Memories IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 10 No6 pp 811-821 November 1988

P88-23 Stefanski P A bull An Introduction to the Computer Facilities of the GMU Center for Artificial Intelligence Reports of the Machine Learning and Inference Laboratory MLI 88-9 George Mason University Fairfax V A November 1988

P88-24 Reinke RE and Michalski RS Incremental Learning of Concept Descriptions A Method and Experimental Results Machine Intelligence II pp 263-288 IE Hayes D Michie and J Richards (Eds) Oxford Clarendon Press 1988

P88-25 Sinclair JB and Michalski RS Computer-Based Consulting System For Diagnosing Soybean Diseases Depts of Plant Pathology and Computer Science University of lllinois Urbana-Champaign 1988

P88-26 Wechsler H and Zimmennan L Disuibuted Associative Memories and Data Fusion Proceedings of the IEEE Second International Conference on Neural Networks Boston MAt November 1988

P88-27 Channic T TEXPERT An Application of Machine Learning to Texture Recognition MS Thesis University of illinois Urbana-Champaign 1988

P88-28 Carbonell JG Michalski RS and Mitchell TM bull Machine learning A Historical and Methodological Analysis Readings from AI Magazine Vols 1-5 1980-1985 R Engelmore (Ed) Menlo Park CA American Association for ArtifICial Intelligence pp 400-408 1988

P88-29 Bratko I Mozetic I and Lavrac N Automatic Synthesis and Compression of Cardiological Knowledge JE Hayes D Michie J Richards (Eds) Machine Intelligence II Oxford Clarendon Press pp 435-454 1988

P88-30 Pipitone F De Jong KA Spears W and Marrone M The FIS Electronics Troubleshooting Project Expert Systems Applications to Telecommunications Liebowitz (Ed) Wiley and Sons pp 73-101 1988

P88-31 De J ong KA bull Learning with Genetic Algorithms An Overview Machine Learning Vol 3 pp 121-138 1988

P88-32 Michalski RS On the Nature of Learning Problems and Research Directions Informatyka No1 and No2 (polish translation by E Pierzchala and P Zielczynski) 1988_

P88-33 Michalski RS bull Ko H and Chen K Qualitative Prediction SPARCJG Methodology for Inductively Describing and Predicting Discrete Processes in Current Issues in Expert Systems Van Lamsweerde A and Dufour O (Eds) 1988

P88-34 Medin D bull Wattenmaker WD and Michalski RS Constraints and Preferences in Inductive Learning An Experimental Study Comparing Human and Machine Performance Cognitive Science 1988

P88-35 Michalski RS bull Learning Strategies and Automated Knowledge ACQuisition An Overview Chapter in the Book Computational Models ofLearning Edited by Leonard Bole 1988

P88-36 Michalski RS and Ko H On the Nature of Explanation Proceedings of the Symposium on the Explanation-based Learning Stanford University March 21-231988

P88-37 Antsaklis PJbull De Jong KAbull Meyrowitz ALbull Meystel A Michalski RS bull Sutton RSbull Machine Learning in a Dynamic World Panel Discussion (edited by M Kokar) Proceedings of the IEEE International Symposium on InteUigent Control Stephanou HE Meystel A bull Luh JYS (Eds) Arlington VA 24-26 August 1988

1989 P89-1

C Kodratoff Y middotmiddotCharacterizing Machine Learning Programs A European Compilation ) 1 Reports of the Machine Learning mul Inference Laboratory MIl 89-1 George Mason

University Fairfax VA February 1989

P89-2 Stefanski PA and Wnek J Bibliography Maintenance System Reports of the Machine Learning and Inference Laboratory MIl 89-2 George Mason University Fairfax VA March 1989

P89-3 Carpineto C Inductive Refmement of Causal Theories Reports of the Machine Learning and Inference Laboratory MLI 89-3 George Mason University Fairfax V A March 1989

P89-4 Mozetic I Hierarchical Model-Based Diagnosis Reports of the Machine Learning and Inference Laboratory MLI 89-4 George Mason University Fairfax V ~ April 1989

P89-5 Pachowicz PW Comparison of Small Autonomous Robots by the Analysis of Their Functional Components Reports of the Machine Learning and Inference Laboratory MLI 89-5 George Mason University Fairfax V ~ 1989

P89-6 Swangwanna S and Zytkow JM Real-Tune Decision Making for Autonomous Flight Control SAE Technical Paper Series 891053 General Aviation Aircraft Meeting amp Exposition Wichita Kansas pp 1-7 April 1989

P89-7 De Jong KA and Spears WA Using Genetic Algorithms to Solve NP-Complete Problems Proceedings of the Third International Conference on Genetic Algorithms and their Applications pp 124-132 George Mason University Fairfax V ~ June 1989

P89-8 Kelly Jr JD PRS A System for Plausible Reasoning MS Thesis University of illinois Urbana-Champaign 1989

P89-9 Zhang J and Michalski RS A Description of Preference Criterion in Constructive Learning A Discussion of Basic Issues Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 17-19 June 1989

P89-10 Tecuci G and Kodratoff Y Multi-strategy Learning in Non-homogeneous Domain Theories Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 14-16 June 1989

P89-11 Stephanou HE and Erkmen AM Shape and Curvature Data Fusion by Conductivity Analysis tI NATO ARW Multisensor Fusion for Computer Vision Grenoble France June 1989

P89-12 Wechsler H and Zimmennan GL Distributed Associative Memory(DAM) for BinshyPicking IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 11 No8 pp 814-822 August 1989

P89-13 Kaufman KA Michalski RS and Kerschberg Lt Mining for Knowledge in Databases Goals and General Description of the INLEN System Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-14 Michalski RS and Littman DC Future Directions of AI in a Resource-Limited Environment Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P88-11 Whitehall BL Substructure Discovery of Macro-Operators Reports 0 CoordinaJed Science Laboratory UlLU-ENG-88-2219 College of Engineering University of lllinois Urbana-Champaign May 1988

P88-12 Nowicki AR A Methodology for Representing Natural Language Expressions in VariableshyValued Logic Reports 0 the Machine Learning and Inference Laboratory MLI 88-3 George Mason University Fairfax V A June 1988

P88-13 Greene GH The Abacus2 System for Quantitative Discovery Using Dependencies to Discover Non-Linear Terms Reports 0 the Machine Learning and Inference Laboratory MLI 88-4 George Mason University Fairfax VA June 1988

P88-14 De Jong KA and Schultz AC Using Experience-Based Learning in Game Playing Proceedings 0 the Fifth InteTlUltional Conference on Machine Learning Ann Arbor MI Oxford Clarendon Press pp 284-290 June 1988

P88-1S Dontas K APPLAUSE An Implementation of the Collins-Michalski Theory of Plausible Reasoning MS Thesis Computer Science Departtnen~ The University of Tennessee Knoxville TN August 1988

P88-16 Bergadano F Matwin S Michalski RS and Zhang J A General Criterion for Measuring Quality ofConcept Descriptions Reports 0 the Machine Learning and Inference Laboratory MLI 88-S George Mason University Fairfax VA October 1988

P88-17 Bergadano F Matwin S Michalski RS and Zhang J Measuring Quality of Concept Descriptions Proceedings of the Third European Working Session on Learning Glasgow pp 1-14 October 1988

P88-18 Bergadano F Matwin S Michalski RS and Zhang J Representing and Acquiring Imprecise and Context-Dependent Concepts in Knowledge-based Systems Proceedings 0 the 3rd InteTlUltional Symposium on Methodologies for Intelligent Systems pp 270-280 Turin Italy October 1988

P88-19 Bergadano F Matwin S Michalski RS and Zhang Learning Two-Tiered Descriptions of Flexible Concepts A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part I Principles and Methodology Reports 0 the Machine Learning and Inerence Laboratory MLI 88-6 George Mason University Fairfax VA November 1988

P88-20 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of flexible Concepts A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part ll Algorithms and Experiments Reports ofthe Machine Learning and Inference Laboratory MLI 88-7 George Mason University Fairfax VA November 1988

P88-21 Collins A and Michalski RS The Logic of Plausible Reasoning A Core Theory Reports of the Machine Learning and Inference lAboratory MLI 88-8 George Mason University Fairfax VA November 1988

P88-22 Wechsler H and Zimmennan GL bull 2_D Invariant Object Recognition Using Disuibuted Associative Memories IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 10 No6 pp 811-821 November 1988

P88-23 Stefanski P A bull An Introduction to the Computer Facilities of the GMU Center for Artificial Intelligence Reports of the Machine Learning and Inference Laboratory MLI 88-9 George Mason University Fairfax V A November 1988

P88-24 Reinke RE and Michalski RS Incremental Learning of Concept Descriptions A Method and Experimental Results Machine Intelligence II pp 263-288 IE Hayes D Michie and J Richards (Eds) Oxford Clarendon Press 1988

P88-25 Sinclair JB and Michalski RS Computer-Based Consulting System For Diagnosing Soybean Diseases Depts of Plant Pathology and Computer Science University of lllinois Urbana-Champaign 1988

P88-26 Wechsler H and Zimmennan L Disuibuted Associative Memories and Data Fusion Proceedings of the IEEE Second International Conference on Neural Networks Boston MAt November 1988

P88-27 Channic T TEXPERT An Application of Machine Learning to Texture Recognition MS Thesis University of illinois Urbana-Champaign 1988

P88-28 Carbonell JG Michalski RS and Mitchell TM bull Machine learning A Historical and Methodological Analysis Readings from AI Magazine Vols 1-5 1980-1985 R Engelmore (Ed) Menlo Park CA American Association for ArtifICial Intelligence pp 400-408 1988

P88-29 Bratko I Mozetic I and Lavrac N Automatic Synthesis and Compression of Cardiological Knowledge JE Hayes D Michie J Richards (Eds) Machine Intelligence II Oxford Clarendon Press pp 435-454 1988

P88-30 Pipitone F De Jong KA Spears W and Marrone M The FIS Electronics Troubleshooting Project Expert Systems Applications to Telecommunications Liebowitz (Ed) Wiley and Sons pp 73-101 1988

P88-31 De J ong KA bull Learning with Genetic Algorithms An Overview Machine Learning Vol 3 pp 121-138 1988

P88-32 Michalski RS On the Nature of Learning Problems and Research Directions Informatyka No1 and No2 (polish translation by E Pierzchala and P Zielczynski) 1988_

P88-33 Michalski RS bull Ko H and Chen K Qualitative Prediction SPARCJG Methodology for Inductively Describing and Predicting Discrete Processes in Current Issues in Expert Systems Van Lamsweerde A and Dufour O (Eds) 1988

P88-34 Medin D bull Wattenmaker WD and Michalski RS Constraints and Preferences in Inductive Learning An Experimental Study Comparing Human and Machine Performance Cognitive Science 1988

P88-35 Michalski RS bull Learning Strategies and Automated Knowledge ACQuisition An Overview Chapter in the Book Computational Models ofLearning Edited by Leonard Bole 1988

P88-36 Michalski RS and Ko H On the Nature of Explanation Proceedings of the Symposium on the Explanation-based Learning Stanford University March 21-231988

P88-37 Antsaklis PJbull De Jong KAbull Meyrowitz ALbull Meystel A Michalski RS bull Sutton RSbull Machine Learning in a Dynamic World Panel Discussion (edited by M Kokar) Proceedings of the IEEE International Symposium on InteUigent Control Stephanou HE Meystel A bull Luh JYS (Eds) Arlington VA 24-26 August 1988

1989 P89-1

C Kodratoff Y middotmiddotCharacterizing Machine Learning Programs A European Compilation ) 1 Reports of the Machine Learning mul Inference Laboratory MIl 89-1 George Mason

University Fairfax VA February 1989

P89-2 Stefanski PA and Wnek J Bibliography Maintenance System Reports of the Machine Learning and Inference Laboratory MIl 89-2 George Mason University Fairfax VA March 1989

P89-3 Carpineto C Inductive Refmement of Causal Theories Reports of the Machine Learning and Inference Laboratory MLI 89-3 George Mason University Fairfax V A March 1989

P89-4 Mozetic I Hierarchical Model-Based Diagnosis Reports of the Machine Learning and Inference Laboratory MLI 89-4 George Mason University Fairfax V ~ April 1989

P89-5 Pachowicz PW Comparison of Small Autonomous Robots by the Analysis of Their Functional Components Reports of the Machine Learning and Inference Laboratory MLI 89-5 George Mason University Fairfax V ~ 1989

P89-6 Swangwanna S and Zytkow JM Real-Tune Decision Making for Autonomous Flight Control SAE Technical Paper Series 891053 General Aviation Aircraft Meeting amp Exposition Wichita Kansas pp 1-7 April 1989

P89-7 De Jong KA and Spears WA Using Genetic Algorithms to Solve NP-Complete Problems Proceedings of the Third International Conference on Genetic Algorithms and their Applications pp 124-132 George Mason University Fairfax V ~ June 1989

P89-8 Kelly Jr JD PRS A System for Plausible Reasoning MS Thesis University of illinois Urbana-Champaign 1989

P89-9 Zhang J and Michalski RS A Description of Preference Criterion in Constructive Learning A Discussion of Basic Issues Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 17-19 June 1989

P89-10 Tecuci G and Kodratoff Y Multi-strategy Learning in Non-homogeneous Domain Theories Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 14-16 June 1989

P89-11 Stephanou HE and Erkmen AM Shape and Curvature Data Fusion by Conductivity Analysis tI NATO ARW Multisensor Fusion for Computer Vision Grenoble France June 1989

P89-12 Wechsler H and Zimmennan GL Distributed Associative Memory(DAM) for BinshyPicking IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 11 No8 pp 814-822 August 1989

P89-13 Kaufman KA Michalski RS and Kerschberg Lt Mining for Knowledge in Databases Goals and General Description of the INLEN System Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-14 Michalski RS and Littman DC Future Directions of AI in a Resource-Limited Environment Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P88-20 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of flexible Concepts A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part ll Algorithms and Experiments Reports ofthe Machine Learning and Inference Laboratory MLI 88-7 George Mason University Fairfax VA November 1988

P88-21 Collins A and Michalski RS The Logic of Plausible Reasoning A Core Theory Reports of the Machine Learning and Inference lAboratory MLI 88-8 George Mason University Fairfax VA November 1988

P88-22 Wechsler H and Zimmennan GL bull 2_D Invariant Object Recognition Using Disuibuted Associative Memories IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 10 No6 pp 811-821 November 1988

P88-23 Stefanski P A bull An Introduction to the Computer Facilities of the GMU Center for Artificial Intelligence Reports of the Machine Learning and Inference Laboratory MLI 88-9 George Mason University Fairfax V A November 1988

P88-24 Reinke RE and Michalski RS Incremental Learning of Concept Descriptions A Method and Experimental Results Machine Intelligence II pp 263-288 IE Hayes D Michie and J Richards (Eds) Oxford Clarendon Press 1988

P88-25 Sinclair JB and Michalski RS Computer-Based Consulting System For Diagnosing Soybean Diseases Depts of Plant Pathology and Computer Science University of lllinois Urbana-Champaign 1988

P88-26 Wechsler H and Zimmennan L Disuibuted Associative Memories and Data Fusion Proceedings of the IEEE Second International Conference on Neural Networks Boston MAt November 1988

P88-27 Channic T TEXPERT An Application of Machine Learning to Texture Recognition MS Thesis University of illinois Urbana-Champaign 1988

P88-28 Carbonell JG Michalski RS and Mitchell TM bull Machine learning A Historical and Methodological Analysis Readings from AI Magazine Vols 1-5 1980-1985 R Engelmore (Ed) Menlo Park CA American Association for ArtifICial Intelligence pp 400-408 1988

P88-29 Bratko I Mozetic I and Lavrac N Automatic Synthesis and Compression of Cardiological Knowledge JE Hayes D Michie J Richards (Eds) Machine Intelligence II Oxford Clarendon Press pp 435-454 1988

P88-30 Pipitone F De Jong KA Spears W and Marrone M The FIS Electronics Troubleshooting Project Expert Systems Applications to Telecommunications Liebowitz (Ed) Wiley and Sons pp 73-101 1988

P88-31 De J ong KA bull Learning with Genetic Algorithms An Overview Machine Learning Vol 3 pp 121-138 1988

P88-32 Michalski RS On the Nature of Learning Problems and Research Directions Informatyka No1 and No2 (polish translation by E Pierzchala and P Zielczynski) 1988_

P88-33 Michalski RS bull Ko H and Chen K Qualitative Prediction SPARCJG Methodology for Inductively Describing and Predicting Discrete Processes in Current Issues in Expert Systems Van Lamsweerde A and Dufour O (Eds) 1988

P88-34 Medin D bull Wattenmaker WD and Michalski RS Constraints and Preferences in Inductive Learning An Experimental Study Comparing Human and Machine Performance Cognitive Science 1988

P88-35 Michalski RS bull Learning Strategies and Automated Knowledge ACQuisition An Overview Chapter in the Book Computational Models ofLearning Edited by Leonard Bole 1988

P88-36 Michalski RS and Ko H On the Nature of Explanation Proceedings of the Symposium on the Explanation-based Learning Stanford University March 21-231988

P88-37 Antsaklis PJbull De Jong KAbull Meyrowitz ALbull Meystel A Michalski RS bull Sutton RSbull Machine Learning in a Dynamic World Panel Discussion (edited by M Kokar) Proceedings of the IEEE International Symposium on InteUigent Control Stephanou HE Meystel A bull Luh JYS (Eds) Arlington VA 24-26 August 1988

1989 P89-1

C Kodratoff Y middotmiddotCharacterizing Machine Learning Programs A European Compilation ) 1 Reports of the Machine Learning mul Inference Laboratory MIl 89-1 George Mason

University Fairfax VA February 1989

P89-2 Stefanski PA and Wnek J Bibliography Maintenance System Reports of the Machine Learning and Inference Laboratory MIl 89-2 George Mason University Fairfax VA March 1989

P89-3 Carpineto C Inductive Refmement of Causal Theories Reports of the Machine Learning and Inference Laboratory MLI 89-3 George Mason University Fairfax V A March 1989

P89-4 Mozetic I Hierarchical Model-Based Diagnosis Reports of the Machine Learning and Inference Laboratory MLI 89-4 George Mason University Fairfax V ~ April 1989

P89-5 Pachowicz PW Comparison of Small Autonomous Robots by the Analysis of Their Functional Components Reports of the Machine Learning and Inference Laboratory MLI 89-5 George Mason University Fairfax V ~ 1989

P89-6 Swangwanna S and Zytkow JM Real-Tune Decision Making for Autonomous Flight Control SAE Technical Paper Series 891053 General Aviation Aircraft Meeting amp Exposition Wichita Kansas pp 1-7 April 1989

P89-7 De Jong KA and Spears WA Using Genetic Algorithms to Solve NP-Complete Problems Proceedings of the Third International Conference on Genetic Algorithms and their Applications pp 124-132 George Mason University Fairfax V ~ June 1989

P89-8 Kelly Jr JD PRS A System for Plausible Reasoning MS Thesis University of illinois Urbana-Champaign 1989

P89-9 Zhang J and Michalski RS A Description of Preference Criterion in Constructive Learning A Discussion of Basic Issues Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 17-19 June 1989

P89-10 Tecuci G and Kodratoff Y Multi-strategy Learning in Non-homogeneous Domain Theories Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 14-16 June 1989

P89-11 Stephanou HE and Erkmen AM Shape and Curvature Data Fusion by Conductivity Analysis tI NATO ARW Multisensor Fusion for Computer Vision Grenoble France June 1989

P89-12 Wechsler H and Zimmennan GL Distributed Associative Memory(DAM) for BinshyPicking IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 11 No8 pp 814-822 August 1989

P89-13 Kaufman KA Michalski RS and Kerschberg Lt Mining for Knowledge in Databases Goals and General Description of the INLEN System Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-14 Michalski RS and Littman DC Future Directions of AI in a Resource-Limited Environment Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P88-30 Pipitone F De Jong KA Spears W and Marrone M The FIS Electronics Troubleshooting Project Expert Systems Applications to Telecommunications Liebowitz (Ed) Wiley and Sons pp 73-101 1988

P88-31 De J ong KA bull Learning with Genetic Algorithms An Overview Machine Learning Vol 3 pp 121-138 1988

P88-32 Michalski RS On the Nature of Learning Problems and Research Directions Informatyka No1 and No2 (polish translation by E Pierzchala and P Zielczynski) 1988_

P88-33 Michalski RS bull Ko H and Chen K Qualitative Prediction SPARCJG Methodology for Inductively Describing and Predicting Discrete Processes in Current Issues in Expert Systems Van Lamsweerde A and Dufour O (Eds) 1988

P88-34 Medin D bull Wattenmaker WD and Michalski RS Constraints and Preferences in Inductive Learning An Experimental Study Comparing Human and Machine Performance Cognitive Science 1988

P88-35 Michalski RS bull Learning Strategies and Automated Knowledge ACQuisition An Overview Chapter in the Book Computational Models ofLearning Edited by Leonard Bole 1988

P88-36 Michalski RS and Ko H On the Nature of Explanation Proceedings of the Symposium on the Explanation-based Learning Stanford University March 21-231988

P88-37 Antsaklis PJbull De Jong KAbull Meyrowitz ALbull Meystel A Michalski RS bull Sutton RSbull Machine Learning in a Dynamic World Panel Discussion (edited by M Kokar) Proceedings of the IEEE International Symposium on InteUigent Control Stephanou HE Meystel A bull Luh JYS (Eds) Arlington VA 24-26 August 1988

1989 P89-1

C Kodratoff Y middotmiddotCharacterizing Machine Learning Programs A European Compilation ) 1 Reports of the Machine Learning mul Inference Laboratory MIl 89-1 George Mason

University Fairfax VA February 1989

P89-2 Stefanski PA and Wnek J Bibliography Maintenance System Reports of the Machine Learning and Inference Laboratory MIl 89-2 George Mason University Fairfax VA March 1989

P89-3 Carpineto C Inductive Refmement of Causal Theories Reports of the Machine Learning and Inference Laboratory MLI 89-3 George Mason University Fairfax V A March 1989

P89-4 Mozetic I Hierarchical Model-Based Diagnosis Reports of the Machine Learning and Inference Laboratory MLI 89-4 George Mason University Fairfax V ~ April 1989

P89-5 Pachowicz PW Comparison of Small Autonomous Robots by the Analysis of Their Functional Components Reports of the Machine Learning and Inference Laboratory MLI 89-5 George Mason University Fairfax V ~ 1989

P89-6 Swangwanna S and Zytkow JM Real-Tune Decision Making for Autonomous Flight Control SAE Technical Paper Series 891053 General Aviation Aircraft Meeting amp Exposition Wichita Kansas pp 1-7 April 1989

P89-7 De Jong KA and Spears WA Using Genetic Algorithms to Solve NP-Complete Problems Proceedings of the Third International Conference on Genetic Algorithms and their Applications pp 124-132 George Mason University Fairfax V ~ June 1989

P89-8 Kelly Jr JD PRS A System for Plausible Reasoning MS Thesis University of illinois Urbana-Champaign 1989

P89-9 Zhang J and Michalski RS A Description of Preference Criterion in Constructive Learning A Discussion of Basic Issues Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 17-19 June 1989

P89-10 Tecuci G and Kodratoff Y Multi-strategy Learning in Non-homogeneous Domain Theories Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 14-16 June 1989

P89-11 Stephanou HE and Erkmen AM Shape and Curvature Data Fusion by Conductivity Analysis tI NATO ARW Multisensor Fusion for Computer Vision Grenoble France June 1989

P89-12 Wechsler H and Zimmennan GL Distributed Associative Memory(DAM) for BinshyPicking IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 11 No8 pp 814-822 August 1989

P89-13 Kaufman KA Michalski RS and Kerschberg Lt Mining for Knowledge in Databases Goals and General Description of the INLEN System Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-14 Michalski RS and Littman DC Future Directions of AI in a Resource-Limited Environment Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P89-4 Mozetic I Hierarchical Model-Based Diagnosis Reports of the Machine Learning and Inference Laboratory MLI 89-4 George Mason University Fairfax V ~ April 1989

P89-5 Pachowicz PW Comparison of Small Autonomous Robots by the Analysis of Their Functional Components Reports of the Machine Learning and Inference Laboratory MLI 89-5 George Mason University Fairfax V ~ 1989

P89-6 Swangwanna S and Zytkow JM Real-Tune Decision Making for Autonomous Flight Control SAE Technical Paper Series 891053 General Aviation Aircraft Meeting amp Exposition Wichita Kansas pp 1-7 April 1989

P89-7 De Jong KA and Spears WA Using Genetic Algorithms to Solve NP-Complete Problems Proceedings of the Third International Conference on Genetic Algorithms and their Applications pp 124-132 George Mason University Fairfax V ~ June 1989

P89-8 Kelly Jr JD PRS A System for Plausible Reasoning MS Thesis University of illinois Urbana-Champaign 1989

P89-9 Zhang J and Michalski RS A Description of Preference Criterion in Constructive Learning A Discussion of Basic Issues Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 17-19 June 1989

P89-10 Tecuci G and Kodratoff Y Multi-strategy Learning in Non-homogeneous Domain Theories Proceedings of the 6th International Workshop on Machine Learning A Segre (Ed) Cornell University Ithaca NY Morgan Kaufmann pp 14-16 June 1989

P89-11 Stephanou HE and Erkmen AM Shape and Curvature Data Fusion by Conductivity Analysis tI NATO ARW Multisensor Fusion for Computer Vision Grenoble France June 1989

P89-12 Wechsler H and Zimmennan GL Distributed Associative Memory(DAM) for BinshyPicking IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 11 No8 pp 814-822 August 1989

P89-13 Kaufman KA Michalski RS and Kerschberg Lt Mining for Knowledge in Databases Goals and General Description of the INLEN System Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-14 Michalski RS and Littman DC Future Directions of AI in a Resource-Limited Environment Proceedings of IJCAJ-89 Workshop on Knowledge Discovery in Databases Detroit MI August 1989

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P89-15 Yegenoglu F and Stephanou HE Collision-Free Path Planning for Multi-robot Systems Proceedings ofthe IEEE International Symposium on Intelligent Control Albany NY September 1989

P89-16 Bergadano F Matwin Sbull Michalski RS and Zhang I bull Learning Flexible Concepts Through a Search for Simpler but Still Accurate Descriptions Proceedings of the Fourth AAAl-Sponsored Knowledge Acquisition for Knowledge-Based Systems Workshop pp 1shy10 Banff Canada October 1989

P89-17 Michalski RS bull Dontas K and Boehm-Davis D bull Plausible Reasoning An Outline of Theory and Experiments Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 17-19 Charlotte NC October 1989

P89-18 Pachowicz PW Low-Level Numerical Characteristics and Inductive Learning Methodology in Texture Recognition Proceedings of the IEEE International Workshop on Tools for ArtificiallnteUigence pp 91-98 Fairfax VA October 1989

P89-19 Stefanski P A and Zytkow IA A Multisearch Approach to Sequence Prediction Proceedings of the Fourth International Symposium on Methodologies for Intelligent Systems pp 359-366 Charlotte NC October 1989

P89-20 Michalski RS Multistrategy Constructive Learning Toward a Unified Theory of Learning Proceedings of ONR Workshop on Knowledge Acquisition Arlington VA November 1989

P89-21 Zytkow IM and Pachowicz PW bull Fusion of Vision and Touch for Spatio-temporal Reasoning in Learning Manipulation Tasks SPIE Symposium on Intelligent Robotics Systems Philadelphia PAt November 1989

P89-22 Zhang I and Michalski RS Rule Optimization Via SG-TRUNC Method Proceedings ofthe Fourth European Working Session on Learning December 1989

P89-23 Collins A and Michalski RS liThe Logic of Plausible Reasoning A Core Theory Cognitive Science Vol 13 pp 1-49 1989

P89-24 De long KA ttAn Artificial Intelligence Approach to Analog Systems Diagnosis in Testing and Diagnosis ofAnalog Systems Van Nostrand-Reinhold 1989

P89-25 Baskin AB and Michalski RS An Integrated Approach to the Construction of Knowledge-Based Systems Experience with ADVISE and Related Programs Topics in Expert System Design G Guida and C Tasso (Eds) New York North-Holland pp 111shy143 1989

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P89-26 Kaufman K bull Michalski RS Zytkow J and Kerschberg L The INLEN System for Extracting Knowledge from Databases Goals and General Description Reports of the Machine lpoundarning and Inference Laboratory MIJ 89-6 George Mason University Fairfax VA1989

P89-27 Kaufman K Michalski RS and Schultz A EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine lpoundaming and Inference Laboratory MIJ 89-7 George Mason University Fairfax VA 1989

P89-28 Fermanian TW bull Michalski RS Katz B and Kelly J AGASSISTANT An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal Vol 81 No2 pp 306-312 1989

P89-29 Fermanian TW and Michalski RS bull WEEDER An Advisory System for the IdentiIlCation of Grasses in Turf Agronomy Journal Vol 81 No2 pp 313-316 1989

P89-30 Michalski RS Two-Tiered Concept Meaning Inferential Matching and Conceptual Cohesiveness Similarity and Analogical Reasoning S Vosniadou and A Ortony (Eds) New York Cambridge University Press 1989

P89-31 Ko H Empirical Assembly Planning A Learning Approach Reports of the Machine lpoundarning and Inference Laboratory MLI 89-8 George Mason University Fairfax V A 1989

P89-32 Kodratoff Y and Tecuci G bull The Central Role of Explanations in DISCIPLE Knowledge Representation Organization in Machine lpoundaming K Morik (Ed) Springer Verlag Berlin pp 135-147 1989

P89-33 Nguyen TN and Stephanou HE A Continuous Model of Robot Hand Preshaping Proceedings of IEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-34 Erkmen AM and Stephanou HE Preshape Jacobians for Minimum Momentum Grasping Proceedings ofIEEE International Conference on Systems Man and Cybernetics Boston MA November 1989

P89-35 Erkmen AM and Stephanou HE Multiresolutional Sensor Fusion by Conductivity Analysis Proceedings of SPiE Symposium on Advances in InteUigent Robotics Systems Philadelphia PA November 1989

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

1990 P90-1 Micbalski RS Multistrategy Constructive Learning Toward Unified Theory of Learning Reports of the Machine Learning and Inference Loboratory MIl 90-1 George Mason University Fairfax VA January 1990

P90-2 Wnek J bull Sarma J Wahab A and Michalski RS bull Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Single Concept Learning using Symbolic Neural Net and Genetic Algorithm Methods Reports of the Machine Learning and Inference Laboratory MLI 90-2 George Mason University Fairfax V A January 1990

P90-3 Wollowski M Learning ICI-Rules through Reporting Differences Reports ofthe Machine Learning and Inference Loboratory MIl 90-3 George Mason University Fairfax VA January 1990

P90-4 Stefanski PA bull Wnek J and Zhang J bull Bibliography of Recent Machine Learning Research 1985-1989 Reports ofthe Machine Learning and Inference Loboratory MIl 90-4 George Mason University January 1990

P90-5 Boehm-Davis D Dontas K and Michalski RS A Validation and Exploration of Structural Aspects of the Collins-Michalski Theory of Plausible Reasoning Reports of the Machine Learning and Inference Laboratory MIl 90-5 George Mason University January 1990

P90-6 De Jong KA Using Neural Networks and Genetic Algorithms as Heuristics for NPshyComplete Problems Proceedings ofIJCNN-90 Washington DC January 1990

P90-7 De Jong KA FIS An AI-based Fault Isolation System Proceedings of IEEE Southeastern 90 New Orleans LA March 1990

P90-8 Piotrowski T On Applying ArtiflCia1 Intelligence Techniques to Building Sea-Going Ships Reports ofthe Machine Learning and Inference Loboratory MLI 90-6 George Mason University March 1990

P90-9 Freeman R PRODIGY Its Exploration and Use Reports of the Machine Learning and Inference Laboratory MLI 90-7 George Mason University May 1990

P90-10 Michalski RS and Kodratoff Y Research in Machine Learning Recent Progress Classiftcation of Methods and Future Directions Machine Learning An ArtificiallnteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 3-30 June 1990

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P90-11 Michalski RS Learning Flexible Concepts Fundamental Ideas and a Method Based on Two-tiered Representation Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 63-111 June 1990

P90-12 Fa1kenhainer BC and Michalski RS Integrating Quantitative and Qualitative Discovery in the ABACUS System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 153-190 June 1990

P90-13 De Jong KA Genetic Algorithm Based Learning Machine Learning An Artificial InteUigence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 611-638 June 1990

P90-14 Kodratoff Y Learning Expert Knowledge by Improving the Explanations Provided by the System Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 433-473 June 1990

P90-15 Tecuci G and Kodratoff Y Apprenticeship Learning in Imperfect Domain Theories Machine Learning An Artificial Intelligence Approach Vol III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 514-552 June 1990

P90-16 Stefanski PA Wnek J and Zhang J Bibliography of Recent Machine Learning Research 1985-1989 Machine Learning An Artificial InteUigence Approach VoL III Y Kodratoff and RS Michalski (Eds) San Mateo CA Morgan Kaufmann Publishers pp 685-789 June 1990

P90-17 Kodratoff Y and Michalski RS (Eds) Machine Learning An Artificial InteUigence Approach Vol III San Mateo CA Morgan Kaufmann Publishers June 1990

P90-18 De Jong KA and Spears W An Analysis of Multipoint Crossover for Genetic Algorithms submitted to Genetic Algorithm Theory Workshop Indiana University June 1990

P90-l9 Pachowicz PW Integrating Low Level Features Computation with Inductive Learning Techniques for Texture Recognition International Journal of Pattern Recognition and Artificial Intelligence Vol 4 No2 pp 147-165 June 1990

P90-20 Bala JW and Pachowicz PWbull Recognizing Noisy Patterns of Texture Via Iterative Optimization and Matching of Their Rule Description Reports of the Machine Learning and Inference Laboratory MLI 90-8 George Mason University June 1990

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P90-21 Pachowicz PW Learning-Based Architecture for the Robust Recognition of Variable Texture to Navigate in Natural Terrain Proceedings IEEE International Workshop on Intelligent Robots and Systems 90 lapan pp 135-142 Iuly 1990

P90-22 Bala IW Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification Proceedings of the Third International Conference on Industrial and Engineering Applications ofAI and Expert Systems Iuly 1990

P90-23 Michalski RS Dontas K and Boehm-Davis D Plausible reasoning An outline of theory and experiments to validate its structural aspects Reports ofthe Machine Learning and Inference Laboratory MLI 90-9 George Mason University Fairfax VA 1990

P90-24 Sibley EH Michael IB and Wexelblat RL Policy Management Economics and Risk Proceedings ofthe IFAC Second International Conference on Economics and Artificial Intelligence Paris France Iuly 1990

P90-25 De long KA Using Genetic Algorithms for Symbolic Learning Tasks Proceedings of the Conference on the Simulation ofAdaptive Behavior Paris France September 1990

P90-26 Wechsler H Computational Vision New York Academic Press September 1990

P90-27 Bergadano F Matwin S Michalski RS and Zhang 1 Learning Two-Tiered Descriptions of Flexible Concepts The POSEIDON System Reports of the Machine Learning and Inference Laboratory MLI 90-10 George Mason University Fairfax VA September 1990

P90-28 Zhang 1 Learning Flexible Concepts from Examples Employing the Ideas of Two-Tiered Concept Representation Reports of the Machine Learning and Inference Laboratory MLI 90-11 George Mason University Fairfax VA September 1990

P90-29 De long KA and Spears WA An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms Conference on Parallel Problem Solving from Nature Dortmund Germany October 1990

P90-30 Wnek I Sarma I Wahab A and Michalski RS Comparing Learning Paradigms via Diagrammatic Visualization A Case Study in Concept Learning Using Symbolic Neural Net and Genetic Algorithm Methods Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems - ISMIS90 Knoxville TN pp 428-437 October 1990

P90-31 Michalski RS A Methodological Framework for Multistrategy Cooperative Learning Proceedings of the 5th International Symposium on Methodologies for Intelligent Systems shyISMIS90 Knoxville TN pp 404-411 October 1990

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P90-32 De Jong KA Using Genetic Algorithms as a Heuristic for NP-Complete Problems Proceedings ofthe ORSAIJIMM Conference New York October 1990

P90-33 Spears WM and De Jong KA Using Genetic Algorithms for Supervised Concept Learning Proceedings ofthe Tools for AI Conference Reston V A November 1990

P90-34 Bala JW and De Jong KA Generation of Feature Detectors for Texture Discrimination by Genetic Search Proceedings ofthe Tools the for AI Conference Reston V A November 1990

P90-35 Dontas K and De Jong KA Discovery of Maximal Distance Codes Using Genetic Algorithms Proceedings ofthe Toolsfor AI Conference Reston VA November 1990

P90-36 Tecuci G A Multistrategy Learning Approach To Domain Modeling and Knowledge Acquisition Reports ofthe Machine Learning and Inference Laboratory MLI 90-12 George Mason University Fairfax VA November 1990

P90-37 Char JM Cherkassky V and Wechsler H bull Fault-Tolerant Database Using Distributed Associative Memories Information Sciences 1990

P90-38 Wechsler H (Ed) Neural Networks for Visual and Machine Perception Oxford University Press 1990

P90-39 Kaufman K Schultz A and Michalski RS EMERALD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the Sun Workstation Reports of the Machine Learning and Inference Laboratory MLI 90-13 George Mason University Fairfax V A December 1990

P90-40 Kodmtoff Y Rouveirol C bull Tecuci G and Duval B Symbolic Approaches to Uncertainty INTELLIGENT SYSTEMS State of the art and future directions ZW Ras and M Zemankova (Eds) 1990

P90-41 Kaufman K and Michalski RS EMERAlD 1 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the VaxStation Reports of the Machine Learning and Inference Laboratory MLI 90-14 George Mason University Fairfax VA December 1990

P90-42 Michalski RS Multistrategy Constructive Learning Toward a Unified Learning Theory invited paper at the ONR Workshop on Knowledge Acquisition Crystal City V A November 6-7 1989 (an extended version appeared in Reports of the Machine Learning and Inference Laboratory MLI 90-1 George Mason University Fairfax VA 1990)

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

1991

P90-43 Michalski RS Theory and Methodology of Inductive Learning in Readings in Machine Learning Dietterich T and Shavlik J (eds) Morgan Kaufmann 1990

P91-1 Michalski RS Searching for Knowledge in a World Flooded with Facts in Applied Stochastic Models and Data Analysis VoL 7 pp 153-163 January 1991

P91-2 Bala JW and Pachowicz PW Application of Symbolic Machine Learning to the Recognition of Texture Concepts Proceedings of the 7th IEEE Conference on Artificial Intelligence Application Miami FL February 1991

P91-3 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Reports of the Machine Learning and Inference Laboratory MLI 91-1 George Mason University Fairfax VA April 1991

P91-4 Pachowicz P W Learning Invariant Texture Characteristics to Dynamic Environments A Model Evolution Re]XJrts of the Machine Learning and Inference Laboratory MLI 91-2 George Mason University Fairfax VA April 1991

P91-5 Tecuci G A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition Proceedings of the European Conference on Machine Learning Y Kodratoff (Ed) Porto Portugal Springer-Verlag 1991

P91-6 Tecuci G and Michalski RS Input Understanding as a Basis for Multistrategy Taskshyadaptive Learning Proceedings of the IntemaJional Symposium on Methodologies for Intelligent Systems Lecture Notes on Artificial Intelligence Z Rag and M Zemankova (Eds)Springer Verlag 1991

P91-7 Michalski RS Searching for Knowledge in a World Flooded with Facts (Invited talk) Proceedings of the Fifth International Symposium on Applied Stochastic Models and Data Analysis Granada Spain April 23-26 1991

P91-8 Kerschberg L and Weishar D An Intelligent Heterogeneous Autonomous Database Architecture for Semantic Heterogeneity Support IEEE Workshop on Interoperability in Multidatabase Systems Kyoto Japan April 1991

P91-9 Bergadano F Matwin S Michalski RS and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Re]XJrts ofthe Machine Learning and Inference Laboratory MLI 91-3 George Mason University Fairfax VA May 1991

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P91-10 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQI7 A Method and Experiments Reports of the Machine Learning and Inference Laboratory MIl 91-4 George Mason University Fairfax VA May 1991

P91-11 Tecuci G and Michalski RS A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications Machine uarning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-12 Pachowicz PW and Bala JW Optimization of Concept Prototypes for the Recognition of Noisy Texture Data Machine Learning Proceedings of the Eighth International Workshop L Birnbaum and G Collins (Eds) San Mateo CA Morgan Kaufmann June 1991

P91-13 Michalski RS Toward a Unified Theory of Learning An Outline of Basic Ideas Invited paper First World Conference on the Fundamentals of Artificial Intelligence Paris France July 1-5 1991

P91-14 Pachowicz PW and Bala JW Texture Recognition Through Machine Learning and Concept Optimization Reports of the Machine Learning and Inference Lohoratory MIl 91shy5 George Mason University Fairfax V A July 1991

P91-15 Tecuci G Steps Toward Automating Knowledge Acquisition for Expert Systems in Proceedings of the AAAJ-9J Workshop on Knowledge Acquisition From Science To Technology to Tools A Rappaport B Gaines and J Boose (Eds) Anaheim CA July 1991

P91-16 Kaufman K Michalski RS and Kerschberg L An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System Proceedings of the AAAJ-9J Workshop on Knowledge Discovery in Databases Anaheim CA July 1991

P91-17 Spears WM and De Jong KA An Analysis of Multi-point Crossover in Foundations ofGenetic Algorithms GJE Rawlins (Ed) Morgan Kaufmann San Mateo July 1991

P91-18 Spears WM and De Jong KA On the Virtues of Parameterized Unifonn Crossover Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann July 1991

P91-19 Wnek J and Michalski RS Hypothesis-Driven Constructive Induction in AQ17 A Method and Experiments Proceedings of the JJCAI-9J Workshop on Evaluating and Changing Representation in Machine uarning Sydney Australia August 1991

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P91-20 De Jong KA and Spears WMbull Learning Concept Classification Rules Using Genetic Algorithms Proceedings ofIJCAI-91 Morgan Kaufmann Sydney Australia August 1991

P91-21 Kerschberg L and Seligman L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems Proceedings of the IJCAI-91 Workshop on Integrating Artificial InteUigence and Databases Sydney Australia August 1991

P91-22 Michalski RS bull Beyond Prototypes and Frames The Two-tiered Concept Representation Reports of the Machine Learning and Inference laboratory MLI 91-6 George Mason University September 1991

P91-23 Tecuci G Automating Knowledge Acquisition As Extending Updating and Improving A Knowledge Base It Reports of the Machine Learning and Inference Laboratory MLI 91-7 George Mason University Fairfax VA September 1991

P91-24 Michael J Validation Verification and Experimentation with Abacus2 Reports of the Machine Learning and Inference laboratory MLI 91-8 George Mason University Fairfax VA September 1991

P91-25 Bala J De Jong KA and Pachowicz P Using Genetic Algorithms to Improve the Performance of Classification Rules Produced by Symbolic Inductive Method Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 Charlotte North Carolina October 16-19 1991

P91-26 Kaufman K Michalski RS and Kerschberg L Knowledge Extraction from Databases Design Principles of the INLEN System Proceedings of the Sixth International Symposium on Methodologies for Intelligent Systems ISMIS91 October 16-19 1991

P91-27 Michalski RS bull Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Reports of the Machine Learning and Inference Laboratory MLI 91-9 George Mason University Fairfax VA October 1991

P91-28 Thrun SB bull Bala J bull Bloedorn E Bratko I Cestnik B Cheng J De Jong KAbull Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS bull Mitchell T Pachowicz P Vafaie H Van de Velde W bull Wenzel W Wnek J and Zhang J bull The MONKs problems A Performance Comparison of Different Learning Algorithms Carnegie Mellon University October 1991

P91-29 Kerschberg L and Baum R bull A Taxonomy of Knowledge-Based Approaches to Fault Management for Telecommunications Networks IEEE Conference on Systems Man and Cybernetics Charlottesville VA October 1991

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P91-30 Pachowicz P Recognizing and Incrementally Evolving Texture Concepts in Dynamic Environments An Incremental Model Generalization Approach Reports of the Machine Learning and Inference Laboratory Mil 91-10 George Mason University Fairfax VA November 1991

P91-31 Mich~ RS and Tecuci G (Eds) Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Machine Learning and Inference Laboratory George Mason University Harpers Ferry WV November 7-9 1991

P91-32 Michalski RS Inferential Learning Theory A Conceptual Framework for Characterizing Learning Processes Proceedings of the First Internatitmal Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-33 Wnek J and Michalski RS An Experimental Comparison of Symbolic and Subsymbolic Learning Paradigms Phase I--Learning Logic-Style Concepts Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-34 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithm Proceedings of the First Internlltional Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-35 Bala J De Jong KA and Pachowicz P Integration of Inductive Learning and Genetic Algorithms to Learn Optimal Concept Descriptions from Engineering Data Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-36 Tecuci G Learning as Understanding the External World Proceedings of the First International Workshop on Multistrategy Learning MSL-91 Harpers Ferry WV November 7-9 1991

P91-37 Bloedorn E and Michalski RS Data Driven Constructive Induction in AQI7-PRE A Method and Experiments Proceedings of the Third International Conference on Tools for AI San Jose CA November 9-14 1991

P91-38 Bala J and Mich~ RS Learning Texture Concepts Through Multilevel Symbolic Transformations Proceedings of the Third International Conference on Tools for Artificial Intelligence San Jose CA November 9-14 1991

P91-39 Janssen T Bloedorn E Hieb MR and Michalski RS Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks An Exploratory Study Proceedings of the Fourth International Symposium on Artificial Intelligence Cancun Mexico November 13-15 1991

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P91-40 Pachowicz PW Application of Symbolic Inductive Learning to the Acquisitions and Recognition of Noisy Texture Concepts in Applications ofLearning and Planning Metlwds November 1991

P91-41 Goma~ H Kerschberg L Bosch C Sugumaran V and Tavakoli I A Prototype Software Engineering Environment for Domain Modeling and Reuse NASAGoddard Sixteenth Annual Software Engineering Workslwp December 4-5 1991

P91-42 Weishar D and Kerschberg Lo DatalKnowledge Packets as a Means of Supporting Semantic Heterogeneity in Multidatabase Systems ACM SIGMOD Record December 1991

P91-43 Kaufman KA Michalski RS and Kerschberg L Mining for Knowledge in Databases Goals and General Description of the INLEN System Knowledge Discovery in Databases G Piatetski-Shapiro and WJ Frawley (Eds) AAAI PressIThe MIT Press Menlo Park CA 1991

P91-44 Hamburger H and Maney T Twofold Continuity in Language Learning ComputershyAssisted Language Learning Vol 4 No2 pp 81-92 1991

P91-45 Pachowicz PW Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision Computer Vision Graphics and Image Processing Academic Press 1991

P91-46 Thrun SoB Bala J Bloedorn E Bratko 1o Cestnik B Cheng J De Jong KA Dzeroski S Fahlman SE Hamann R Kaufman K Keller S Kononenko I Kreuziger J Michalski RS Mitchell T Pachowicz P Vafaie H Van de Velde W 0 Wenzel W Wnek J and Zhang J The MONKts problems A Performance Comparison of Different Learning Algorithms Computer Science Reports CMU-CS-91-197 Carnegie Mellon University (Revised version) Pittsburgh PA December 1991

P91-47 Mihaski RS Kaufman K and Wnek J The AQ Family of Learning Programs A Review of Recent Developments and an Exemplary Application Reports of the Machine Learning and Inference Laboratory MU 91-11 George Mason University Fairfax VA December 1991

P91-48 Bloedorn E and Michalski RS Constructive Induction from Data in AQ17-OCI Further Experiments Reports of the MachiIu Learning and Inference Laboratory MLI 91-12 George Mason University Fairfax VA December 1991

P91-49 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A case study involving soybean pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1991

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

1992 P92-1 Bergadano F Matwin S Michalski R S and Zhang J Learning Two-tiered Descriptions of Flexible Concepts The POSEIDON System Machine Learning Vol 8 No I pp 5-43 January 1992

P92-2 Wnek 1 Version Space Transformation with Constructive Induction The VS Algorithm Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-01 January 1992

P92-3 Wnek J and Michalski RS Hypothesis-driven Constructive Induction in AQ17 A Method and Experiments It Reports of the Machine Learning and Inference Laboratory George Mason University Mil 92-02 January 1992

P92-4 De Jong KA and Spears WMbull A Formal Analysis of the Role of Multi-point Crossover in Genetic Algorithms Annals of Mathematics and Artificial Intelligence Vol 5 No I January 1992

P92-5 Fermanian T and Michalski RS AgriAssistant A New Generation Tool for Developing Agricultural Advisory Systems in Expert Systems in the Developing Countries Practice and Promise Ch K Mann and S R Ruth (Eds) Westview Press Publication 1992

P92-6 Michalski RS Knowledge Acquisition by Encoding Expert Rules versus Computer Induction from Examples A Case Study Involving Soybean Pathology in Artificial Intelligence and Software Engineering Partridge (Ed) Alex Publishing Corporation 1992

P92-7 Michalski RS Kerschberg L Kaufman KA and Ribeiro JS Searching for Knowledge in Large Databases Proceedings of the First International Conference on Expert Systems and Development Cairo Egypt April 1992

P92-8 Bata J Michalski RS and Wnek J The Principal Axes Method for Constructive Induction Proceedings of the 9th International Conference on Machine Learning D Sleeman and P Edwards (Eds) Aberdeen Scotland July 1992

P92-9 Tecuci G Cooperation in Knowledge Base Refmement Proceedings of the Ninth International Machine Learning Conference (ML92J D Sleeman and P Edwards (Eds) Morgan Kaufmann Aberdeen Scotland July 1992

P92-10 Tecuci G and Hieb MR Consistency Driven Knowledge Elicitation Within a Learning Oriented Representation of Knowledge Proceedings of the AAAJ-92 Workshop on Knowledge Representation Aspects ofKnowledge Acquisition Los Angeles CA July 1992

P92-11 De Jong KA and Sarma J~ Generation Gaps Revisited Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

7

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P92-12 De Jong KA Genetic Algorithms are Nor Function Optimizers Proceedings of the Second Workshop on Foundations ofGenetic Algorithms Morgan Kaufmann July 1992

P92-13 Mich~ RS Kerschberg L Kaufman KA and Ribeiro JS Mining For Knowledge in Databases The INLEN Architecture Initial Implementation and First Results Intelligent Information Systems Integrating Artificial Intelligence aruJ Database Technologies Vol 1 No1 pp 85-113 August 1992

P92-14 Kulpa Z and Sobolewski M Knowledge-directed Graphical and Natural Language Interface with a Knowledge-based Concurrent Engineering Environment Proceedings of the 8th International Conference on CADCAM Robotics aruJ Factories of the Future Metz France August 1992

P92-15 Pachowicz PW Bala J and Zhang J Methodology for Iterative Noise-Tolerant Learning and Its Application to Object Recognition in Computer Vision Proceedings of the 6th International Conference on Systems Research Informatics aruJ Cybernetics BadenshyBaden Germany August 1992

P92-16 Pachowicz PW Hieb MR and Mohta P A Learning-Based Incremental Model Evolution for Invariant Object Recognition Proceedings of the 6th International Conference on Systems Research Informatics and Cybernetics Baden-Baden Germany August 1992

P92-17 Tecuci G bull Automating Knowledge Acquisition as Extending Updating and Improving a Knowledge Base in IEEE Transactions on Systems Man and Cybernetics Vol 22 No6 pp 1444-1460 NovemberlDecember 1992

P92-18 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Learning Reports of the Machine Learning aruJ Inference Laboratory George Mason University Mil 92-03 September 1992

P92-19 Wnek J and Mich~ RS Comparing Symbolic and Subsymbolic Learning Three Studies Reports of the Machine Learning aruJ Inference Laboratory George Mason University MLI 92-04 September 1992

P92-20 De Jong K Abull Are Genetic Algorithms Function Optimizers Proceedings of PPSN-92 the 2nd Conference on Parallel Problem Solving from Nature Brussels Belgium ElseviershyHolland September 1992

P92-21 Gomaa H bull Kerschberg L and Sugumaran V A Knowledge-Based Approach to Generating Target Systems Specifications from a Domain Model Proceedings ofIFIP World Computer Congress Madrid Spain September 1992

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P92-22 Vamos T Epistemology Uncertainty and Social Change Reports of the Machine Liaming and Inference Laboratory George Mason University MU 92-05 October 1992

P92-23 Pachowicz PW A Learning-Based Evolution of Concept Descriptions for an Adaptive Object Recognition Proceedings ofthe 4th International Conference on Tools with Artificial IntelUgence pp 316-323 Arlington VA November 1992

P92-24 Pachowicz PW Bal~ I and Zhang I Iterative Rule Simplification for Noise Tolerant Inductive Learning Proceedings ofthe 4th International COnference on Tools with Artificial Intelligence pp 452-453 Arlington VA November 1992

P92-25 Vafaie H and De long KA Genetic Algorithms as a Tool for Feature Selection in Machine Learning Proceedings of the 4th International Conference on Tools with Artificial Intelligence Arlington V A November 1992

P92-26 Seligman L and Kerschberg L Approximate Knowledge BaselDatabase Consistency An Active Database Approach Proceedings ofthe First International Conference on Information and Knowledge Management November 1992

P92-27 Yoon IP and Kerschberg L A Framework for Constraint Management in ObjectshyOriented Databases Proceedings of the First International Conference on Information and Knowledge Management November 1992

P92-28 Crain S and Hamburger H Semantics Knowledge and NP Modification In Formal Grammar Theory and Implementation R Levine (Ed) Oxford University Press Oxford England 1992

P92-29 Hamburger H and Hashim R Foreign Language Tutoring and Learning Environment In Intelligent Tutoring Systems for Foreign Language Liaming M Swartz and M Yazdani (Eds) Springer Verlag New York amp Berlin 1992

P92-30 Hamburger H and Lodgher A Semantically Constrained Exploration and Heuristic Guidance In Intelligent Instruction by Computer I Psotka and M Farr (Eds) Taylor and Francis New York 1992

P92-31 Hashim R and Hamburger H Discourse Style and Situation Viewpoint for a Conversational Language Tutor Proceedings of the International Conference on ComputershyAssisted Learning Wolfville Nova Scoti~ Canada Springer-Verlag New York 1992

P92-32 Pan I and Hamburger H A Knowledge-based Learning System for Chinese Character Writing Proceedings of the International Conference on Computer Processing of Chinese and Oriental Languages Clearwater Beach FL December 15-19 1992

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

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P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P92-33 Hieb MR and Tecuci G Two Methods for Consistency-driven Knowledge Elicitation Reports of the Machine Learning and Inference Laboratory George Mason University MLI 92-06 December 1992

P92-34 Arciszewski T Bloedorn E Michalski RS bull Mustafa M and Wnek 1 Constructive Induction in Structural Design Reports of the Machine Learning and Inference Laboratory George Mason University MU 92-07 December 1992

P92-35 Tecuci G and Hieb MR Consistency-driven Knowledge Elicitation Using a Machine Learning Oriented Knowledge Representation to Integrate Learning and Knowledge Elicitation in NeoDISCIPLEt Reports of the Machine Learning and Inference Laboratory MLI 92-08 December 1992

P92-36 Arciszewski T Dybala T and Wnek 1bull ttA Method for Evaluation of Learning Systems HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning VoL 5 No4 pp 22-311992

P92-37 Hieb MR Silverman BG and Mezher TM ttRule Acquisition for Dynamic Engineering Domains HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 72-82 1992

P92-38 Wnek 1 and Michalski RS bull Experimental Comparison of Symbolic and Subsymbolic Learning HEURISTICS The Journal of Knowledge Engineering Special Issue on Knowledge Acquisition and Machine Learning Vol 5 No4 pp 1-21 1992

P92-39 Bala 1 W and Pachowicz P Recognizing Noisy Pattern VJa Iterative Optimization and Matching of Their Rule Description International Journal on Pattern Recognition and Artificial Intelligence Vol 6 No4 1992

P92-40 Michalski RS LEARNlNG = INFERENCING + MEMORIZING Basic Concepts of Inferential Theory of Learning and Their Use for Classifying Learning Processes in Cognitive Models ofLearning Chipman S and Meyrowitz A (Eds) 1992

P92-41 Balalbull Bloedorn E bull De long K bull Kaufman K Michalski RS bull Pachowicz P bull Vafaie H Wnek 1 and Zhang 1 A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems MLI92-08 December 1992

v

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

1993 P93-1

[1 MichaJski RS Toward a Unified Theory of Learning Multistrategy Task-adaptive Learning in Readings in Knowledge Acquisition and Learning Automating the Construction and Improvement ofExpert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-2 Michalski RS A Theory and Methodology of Inductive Learning in Readings in Knowledge Acquisition and lpoundarning Automating the Construction and Improvement of Expert Systems BG Buchanan and DC Wilkins Morgan Kaufmann San Mateo 1993

P93-3 Van Mechelen I Hampton I Michalski RS and Tbeuns P (Eds) Categories and Concepts Theoretical Views and Inductive Data Analysis Academic Press New York 1993

P93-4 Michalski RS ItBeyond Prototypes and Frames The Two-tiered Concept Representation in Categories and Concepts Theoretical Views and Inductive Data Analysis 1 Van Mecbelen I Hampton RS Michalski and P Theuns (Eds) Academic Press New York 1993

P93-S Michalski RS Learning =Inferencing + Memorizing Introduction to Inferential Theory of Learning in Foundations ofKnowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-6 MichaJski RS Bergadano F bull Matwin S and Zhang I Learning Flexible Concepts Using a Two-tiered Representation in Foundations of Knowledge Acquisition VoL 2 Machine Learning S Chipman and A Meyrowitz (Eds) 1993

P93-7 MichaJski RS bull Pachowicz PW bull Rosenfeld A and Aloimonos Y Machine Learning and Vision Research Issues and Promising Directions NSFIDARPA Workshop on Machine Learning and Vision (MLV-92) Harpers Ferry WV October IS-17 1992 Reports of the Machine Learning and Inference Loboratory MLI 93-1 George Mason University February 1993

P9y~ Wnek I Hypothesis-driven Constructive Induction PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference lllboratory MLI 93-2 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-9 Bala IW Learning to Recognize Visual Concepts Development and Implementation of a Method for Texture Concept Acquisition Through Inductive Learning PhD dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Loboratory MLI 93-3 George Mason University (also published by University Microfilms Int Ann Arbor MI) March 1993

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

( I

I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P93-10 Michalski RS Bala JW and Pachowicz PW GMU Research on Learning in Vision Initial Results Proceedings of the DARPA Image Understanding Workshop Washington DC April 18-21 1993

P93-11 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Reports of the Machine Learning and Inference lAboratory MIl 93-4 George Mason University May 1993

P93-12 Hieb M and Michalski RS Knowledge Representation Based on Dynamically Interlaced Hierarchies Basic Ideas and Examples Re]XJrts of the Machine Learning and Inference Laboratory MLI 93-5 George Mason University May 1993

P93-13 Bloedorn E Wnek J and Michalski RS Multistrategy Constructive Induction Proceedings of the Second Internationol Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-291993

P93-14 Hieb M and Michalski RS Knowledge Representation for Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Proceedings of the Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-15 Michalski RS and Tecuci G (Eds) Proceedings ofthe Second International Workshop on Multistrategy Learning (MSL93) Harpers Ferry WV Morgan Kaufmann May 27-29 1993

P93-16 Imam IF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Reports of the Machine Learning and Inference Laboratory MLI 93-6 George Mason University May 1993

P93-17 Wnek J Michalski RS and Arciszewski T An Application of Constructive Induction to Engineering Design Re]XJrts ofthe Machine Learning and Inference lAboratory MIl 93-7 George Mason University May 1993

P93-18 Imam IF and Michalski RS Should Decision Trees Be Learned from Examples or from Decision Rules Lecture Notes in ArtijicialInteUigence Springer Verlag Proceedings of the 7th Intemationol Symposium on Methodologies for Intelligent Systems ISMIS Trondheim Norway June 15-18 1993

P93-19 Imam IF Michalski RS and Kerschberg L Discovering Attribute Dependence in Databases by Integrating Symbolic Learning and Statistical Analysis Techniques Proceedings of the AAAJ-93 Workshop on Knowledge Discovery in Databases Washington DC July 11-12 1993

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P93-20 Michalski RS and Tecuci G Multistrategy Learning Tutorial at the National Conference on Artificial Intelligence AAAI-93 Washington DC July 11-12 1993

P93-21 Michalski RS bull Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning Special Issue on Multistrategy Learning VoL 11 pp 111-151 1993

P93-22 Wnek J bull Michalski RS and Arciszewski T bull An Application of Constructive Induction to Engineering Design Proceedings of the IJCAI-93 Workshop on AI in Design Chambery France August 1993

P93-23 Michalski RS and Tecuci G bull Multistrategy Learning Tutorial at the International Joint Conference on Artificial Intelligence IJCAI-93 Chambery France August 1993

P93-24 Kaufman KA Schultz A and Michalski RS EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Users Guide Reports of the Machine Learning and Inference Laboratory MLI 93-8 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-25 Kaufman KA Michalski RS and Schultz A EMERAlD 2 An Integrated System of Machine Learning and Discovery Programs for Education and Research Programmers Guide for the SUN Workstation Reports of the Machine Learning and Inference Laboratory MIl 93-9 Machine Learning and Inference Laboratory George Mason University Fairfax VA September 1993

P93-26 Kaufman KA and Michalski RS EMERAlD An Integrated System o~hine Learning and Discovery Programs to Support Education and Experimental Reseacenth Rtports of the Machine Learning and Inference Laboratory MLI 93-10 MaGhine Learning and Inference Laboratory George Mason University Fairfax V A September 1993 ~

P93-27 Bala J bull Michalski RS and Wnek J bull The PRAX Approach to Learning a Large Number of Texture Concepts Technical Report FS-93-04 Machine Learning in Computer VISion What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-28 Bata J and Pachowicz PW Issues in Learning from Noisy Sensory Data Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Learning in Computer Vision AAAI Press Menlo Park CA October 1993

P93-29 Pachowicz P W Integration of Machine Learning and Vision into an Active Agent Paradigm on the Example of Face Recognition Problem Technical Report FS-93-04 Machine Learning in Computer Vision What Why and How AAAI Fall Symposium on Machine Leaming in Computer Vision AAAI Press Menlo Park CA October 1993

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I

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

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V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P93-30 ImamIF and Michalski RS Learning Decision Trees from Decision Rules A Method and Initial Results from a Comparative Study Journal of Intelligent Information Systems JllS L Kerschberg Z Ras and M Zemankova (Eds) Vol 2 No3 pp 279-304 Kluwer Academic Boston MA 1993

P93-31 Vafaie H and De long KA Robust Feature Selection Algorithms Proceedings of the 5th International Conference on Tools with Artificial Intelligence Boston MA November 1993

P93-32 Michalski RS and Wnek 1 Constructive Induction An Automated Design of Knowledge Representation Spaces for Machine Learning Reports ofthe Machine Learning and Inference lAboratory MLI 93-11 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-33 Bloedorn E Wnek I Micha1ski RS and Kaufman K AQI7 A Multistrategy Learning System The Method and Users Guide Reports of the Machine Learning and Inference lAboratory MLI 93-12 Machine Learning and Inference Laboratory George Mason University Fairfax VA November 1993

P93-34 Guillen LE Ir and Wnek 1 Investigation of Hypothesis-driven Constructive Induction in AQI7 Reports of the Machine Learning and Inference Laboratory Mil 93-13 Machine Learning and Inference Laboratory George Mason University Fairfax VA December 1993

P93-35 Hieb MR and Micha1ski RS Multitype Inference in Multistrategy Task-adaptive Learning Dynamic Interlaced Hierarchies Informatica An International Journal of Computing and Informatics Vol 17 No4 pp 399-412 December 1993

P93-36 Michalski RS and Tecuci G Multistrategy Learning in Encyclopedia of Microcomputers Vol 12 A Kent and IG Williams (Eds) Marcel Dekker New York 1993

P93-37 Michalski RS and Wnek 1 Constructive Induction An Automated Improvement of Knowledge Representation Spaces for Machine Learning Proceedings of the 2nd Conference on Practical Aspects ofArtificialIntelligence pp 188-236 Augustow IPI PAN Warszawa Poland 1993

P93-38 Khasnabis S Arciszewski T Hoda SK and Ziarko W Automated Knowledge Acquisition for Control of an Urban Rail Conidor Proceedings of the Third International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering Edinburgh Scotland 1993

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P93-39 Arciszewski T and Usmen M Applications of Machine Learning to Construction Safety Proceedings of the International Conference on Management of Information Technology for Construction Singapore 1993

P93-40 Arciszewski T Ziarko W and Khan T L Learning Conceptual Design Rules A Rough Sets Approach Proceedings of the International Workshop on Rough Sets Banff Alberta Canada 1993

P93-41 Arciszewski T tlLearning Engineering An Outline Proceedings of the ASCE Conference on Computing in Civil Engineering Anaheim California 1993

P93-42 Seligman L and Kerschberg L An Active Database Approach to Consistency Management in Heterogeneous Data- and Knowledge-based Systems International Journal ofCooperative and Intelligent Systems Vol 2 No2 October 1993

P93-43 Seligman L and Kerschberg L Federated Knowledge and Database Systems A New Architecture for Integrating of AI and Database Systems in Advances in Databases and Artificial Intelligence Vol 1 The Landscape of Intelligence in Database and Information Systems L Delcambre and F Petry (ampis) JAI Press 1993

P93-44 Yoon JP and Kerschberg L A Framework for Knowledge Discovery and Evolution in DatabasestI IEEE Transactions on Knowledge and Data Engineering Vol 5 No6 December 1993

P93-45 Michalski RS Carbonell J Mitchell T and Kodratoff Y (Eds) Apprentissage Symbolique Une Approche de I1ntelligence Artificielle Tome I-II French compilation of Machine Learning An Artificial Intelligence Approach Vol I-III Cepadues-Editions 1993

P93-46 Michalski RS (Ed) Multistrategy Learning Kluwer Academic Publishers 1993

P93-47 Michaels GS Taylor R Hagstrom R Price M and Overback R Searching for Genomic Organizational Motifs Explorations of the E coli Chromosome Computers in Chemistry Vol 17 pp209-217 1993

P93-48 Michaels GS Taylor R t Hagstrom R Price M and Overback R t Comparative Analysis of Genomic Data A Global Look and StructuraI and Regulatory Features Proceedings of the Second International Conference of Bioinformatics Supercomputing and Complex Genome Analysis Hua A Lim (Ed) World Scientific Publishing Co River Edge NJ pp 297-308 1993

1994 P94-1 Michalski RS and Tecuci G (Eds) Machine Learning A Multistrategy Approach VoL IV Morgan Kaufmann San Mateo CAt 1994

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P94-2 Bala J W De Jong KA and Pachowicz P Multistmtegy Uarning from Engineering Data by Integrating Inductive Generalization and Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-3 De Garis H Genetic Programming Evolutionary Approaches to Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-4 Michalski RS Inferential Theory of Learning Developing Foundations for Multistrategy Uarning in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-5 Vafaie H and De Jong KA Improving the Performance of a Rule Induction System Using Genetic Algorithms in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-6 Wnek J and Michalski RS ttComparing Symbolic and Subsymbolic Learning Three Studies in Machine Learning A MuItistrategy Approach Vol 4 RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-7 Wnek J and Hieb M Bibliography of Multistrategy Learning Research in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-8 Zhang 1 Learning Graded Concept Descriptions by Integrating Symbolic and Subsymbolic Approaches in Machine Learning A Multistrategy Approach Vol IV RS Michalski and G Tecuci (Eds) Morgan Kaufmann San Mateo CA 1994

P94-9 WnekJ and Michalski RS Hypothesis-driven Constructive Induction in AQI7-HCI A Method and Experiments Machine Learning Vol 14 No2 pp 139-168 1994

P94-10 Vafaie H and Imam IP Feature Selection Methods Genetic Algorithm vs Greedy-like Search Proceedings of the 3rd InternaJional Fuzzy Systems and Intelligent Control Conference Louisville KY March 1994

P94-11 Wnek J and Michalski RS Symbolic Learning of M-of-N Concepts Reports of the Machine Learning and Inference Laboratory MLI 94-1 Machine Learning and Inference Laboratory George Mason University Fairfax VA April 1994

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P94-12 Bloedorn E Michalski RS and Wnek I Matching Methods with Problems ~

Comparative Analysis of Constructive Induction Approaches Reports of the MQC 9rfe--ni

Learning and Inference Loboratory Ml1 94-2 Machine Learning and Inference LaboratqrY t

George Mason University Fairfax VA May 1994 i shy

P94-13 Imam IF and Vafaie H An Empirical Comparison Between Global and Greedy-like Search for Feature Selection Proceedings of the 7th Florida Anificiallntelligence Research Symposium (FLAlRS-94) pp 66-70 Pensacola Beach FL May 1994

P94-14 Tischer L and Bloedorn E An Application of Machine Learning to GIS Analysis Proceedings ofthe ESRl-94 User Conference CA May 1994

P94-15 Imam IF An Experimental Study of Discovery in Large Temporal Databases Proceedings of the Seventh International Conference on Industrial and Engineering Applications of Anificial Intelligence and Expert Systems (IEAIAlE-94) Austin TX pp 171-180 Iune 1994

P94-16 Arciszewski T Bloedorn E Michalski RS Mustafa M and Wnek I Machine Learning of Design Rules Methodology and Case Study ASCE Journal of Computing in Civil Engineering Vol 8 No3 pp 286-308 Iuly 1994

P94-17 Sazonov VN and Wnek I Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change ofRepresentation New Brunswick NI Iuly 1994

P94-18 Wnek I and Michalski RS Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules Working Notes of the ML-COLT94 Workshop on Constructive Induction and Change of Representation New Brunswick NI Iuly 1994

P94-19 Arciszewski T Khasnabis S Hoda SK and Ziarko W Machine Learning in Transportation Engineering A Feasibility Study Journal of Applied Artijiciallntelligence Vol 8 No1 1994

P94-20 Arciszewski T Borkowski A Dybala T Racz 1 and Wojan P Empirical Comparison for Symbolic and Subsymbolic Learning Systems Proceedings of the First International ASCE Congress on Computing in Civil Engineering Washington DC 1994

P94-21 Arciszewski T Machine Learning in Engineering Design Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P94-22 Arciszewski T and Michalski RS Inferential Design Theory A Conceptual Outline Proceedings of the Third International Conference on Anificial Intelligence in Design Lausanne Switzerland 1994

P94-23 Imam IF and Michalski RS From Fact to Rules to Decisions An Overview of the FRDshy1 System in the Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 229-236 Seattle WA August 1994

P94-24 Kaufman K Comparing International Development Patterns Using Multi-operator Learning and Discovery Tools Proceedings of the AAA-94 Workshop on Knowledge Discovery in Databases pp 431-440 Seattle WA August 1994

P94-25 Maloof M and Michalski RS Learning Descriptions of 2D Shapes for Object Recognition and X-Ray Images Reports ofthe Machine Learning and Inference Laboratory MLI 94-4 George Mason University Fairfax VA October 1994

P94-26 Michalski RS and Ram A Learning as Goal-Driven Inference Reports of the Machine Learning and Inference Laboratory MLl94-5 George Mason University Fairfax VA April 1994

P94-27 Michalski RS Rosenfeld A and Aloimonos Y Machine Vision and Learning Research Issues and Directions Reports of the Machine Learning and Inference Laboratory MLI 94-6 George Mason University Fairfax VA Reports of the Center for Automation Research CAR-TR-739 CS-TR-3358 University of Maryland College Park MD October 1994

P94-28 Michalski RS and Imam IF Learning Problem-Oriented Decision Structures from Decision Rules The AQDT-2 System in ucture Notes in Anificial Intelligence Methodology for Intelligent Systems ofthe 8th International Symposium on Methodology for Intelligent Systems (ISMIS-94) zw Ras amp M Zemankova(Eds) No 869 pp 416-426 October 1994 1 P94-29 it Michaels GS Bioinformatics or Biology Chemical Design Automation News Vol 8 pp 1-34 1994

P94-30 Zull JE Taylor RC Michaels GS and Rushforth N Nucleic Acid Sequences Coding for Internal Antisence Peptides Are There Implications for Protein Folding and Evolution Nucleic Acid Research 1994 (P94-31 Wnek J and Michalski RS Conceptual Transition from Logic to Arithmetic Reports of

[Machine Learning and Inference Laboratory MLI 94-7 t~Ji Jl~iJJ George Mason University Fairfax VA December 1994 shy gt

i

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P94-32 Michalski RS ttSeeking Knowledge in the Flood of Facts Proceedings of the Conference on Intelligent Information Systems Institute of Computer Science Polish Academy of Sciences Wigry Poland 1994

P94-33 Pachowicz PW and Bala JW ttA Noise-Tolerant Approach to Symbolic Learning from Sensory Data Journal ofIntelligent and Fuzzy Systems Vol 2 pp 347-361 John Wiley amp Sons Inc 1994

P94-34 Bala JW Pachowicz PW and Michalski RS Progress on Vision Through Learning at GeOrge Mason University Proceedings of the ARPA Image Understanding Workshop November 13-16 1994

1995 P95-1 Michalski RS and Wnek J (Eds) Center for Machine Learning and InferenceAn Overview of Research and Activities Reports of the Machine Learning and Inference Laboratory MLI 95-1 George Mason University Fairfax VA January 1995

P95-2 Maloof M and Michalski RS itA Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection Reports ofthe Machine Learning and Inference Laboratory MLI95-2 George Mason University Fairfax VA March 1995

P95-3 Bloedorn E Imam I Kaufman K Maloof M Michalski RS and Wnek J HOW DID AQ FACE THE EAST-WEST CHAILENGE An Analysis of the AQ Familys Performance in the 2nd International Competition of Machine Learning ProgramsII Reports of the Machine Learning and Inference Laboratory MIl 95-3 George Mason University Fairfax VA March 1995

P95-4 Wnek J Kaufman K Bloedorn E and Michalski RS Inductive Learning System AQI5c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 95-4 George Mason University Fairfax VA March 1995

P95-5 Wnek J IDIAV 20 User Manual Specification and Guide through the Diagrammatic Visualization System Reports ofthe Machine Learning and Inference Laboratory MLI 95-5 George Mason University Fairfax VA 1995

P95-6 Arciszewski T Michalski R and Dybala T STAR Methodology-Based Learning about Construction Accidents and their Prevention Journal of Construction Automation Vol 4 pp 75-85 1995

P95-7 Imaml and Wnek J (Eels) Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Melbourne Beach FL April 26 1995

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

V

P95-8 Bloedorn E and Wnek J Constructive Induction-based Learning Agents An Architecture and Preliminary Experiments Proceedings ofthe First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 38-49 Melbourne Beach FL April 26 1995

P95-9 Imam I tlIntelligent Agents for Management of Learning An Introduction and a Case Study Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) pp 95-106 Melbourne Beach FL April 26 1995

P95-10 Arciszewski T Michalski RS and Wnek 1 Constructive Induction The Key to Design Creativity Reports of the Machine Learning and Inference lAboratory MLI 95-6 George Mason University Fairfax VA April 1995

P95-11 Chen Q and Arciszewski T Machine Learning of Bridge Design Rules A Case Study Proceedings of the 2nd ASCE Congress on Computing in Civil Engineering Atlanta June 1995

P95-12 Ribeiro J Kaufman K and Kerschberg L Knowledge Discovery from Multiple Databases Proceedings of the IASTEDIISMM International Conference on Intelligent Information Management Systems Washington DC June 1995

P95-13 Michalski RS and Wnek J Learning Hybrid Descriptions Proceedings of the 4th International Symposium on Intelligent Information Systems Augustow Poland June 5-9 1995

P95-14 Michalski RS Learning and Cognition Invited talk at the 2nd International World Conference on the Foundations ofArtijiciallntelligence Paris July 3-7 1995

P95-15 Szczepanik W Arciszewski T and Wnek J Empirical Performance Comparison of Two Symbolic Learning Systems Based On Selective And Constructive Induction Proceedings of the IJCAJ-95 Workshop on Machine Learning in Engineering Montreal Canada August 1995

P95-16 Ribeiro J bull Kaufman K and Kerschberg Lbull Knowledge Discovery from Multiple Databases Proceedings of the First International Conference on Knowledge Discovery in Data Mining (KDD-95) Montreal Canada August 1995

P95-17 Maloof MA and Michalski RS Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images Proceedings of the 8th International Symposium on Artijiciallntelligence Monterrey Mexico October 17-201995

P95-18 Maloof MA and Michalski RS A Method for Partial Memory Incremental Learning and its Application to Intrusion Detection Proceedings of the 7th IEEE International Conference on Tools with Artijiciallntelligence Herdon VA 1995

r--Y

V

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P95-19 Vafaie H and De Jong KA Genetic Algorithm as a Tool for Restructuring Feature Space Representations Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence Henion VA 1995

P95-20 Imam IMF Deriving Task-oriented Decision Structures From Decision Rules PhD dissertation Machine Learning and Inference Laboratory Reports of the Machine Learning and Inference Laboratory MLI 95-7 George Mason University Fairfax VA October 1995

P95-21 Michalski RS and Ram A Learning as Goal-Driven Inference in Goal-Driven Learning ARam amp DBLeake (Eds) MIT PressIBradford Books Cambridge MA 1995

P95-22 Maloof MA and Michalski RS Learning Evolving Concepts Using a Partial Memory Approach Proceedings oftheAAAl 1995 Fall Symposium on Active Learning Cambridge MA November 10-12 1995

P95-23 Arciszewski T Michalski RS Wnek J bull Constructive Induction the Key to Design Creativity Proceedings ofthe Third International Round-Table Conference on Computational Models oCreative Design pp 397-425 Heron Island Queensland Australia December 3shy7 1995

P95-24 Zhang J and Michalski RS An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning Vo121 No3 pp 235-268 Kluwer Academic Publishers December 1995

P95-25 Michalski RS Wnek J Presentation Notes of the Annual Review of Research in i(Machine Learning and Inference Machine Learning and Inference Laboratory George Mason University Fairfax VA May 19 1995

P95-26 Arciszewski T Bloedorn E bull Michalski RS bull Mustafa M and Wnek J bull Machine Learning in Engineering Design A Methodology and Case Study Reports of the Machine Learning and Inference Laboratory MLI 95-8 George Mason University Fairfax VA December 1995

P96-1 Michalski RS and Wnek J (Eds) Machine Learning and Inference LaboratoryAn Overview of Research and Activities Reports 0 the Machine Learning and Inference Laboratory MLI 96-1 George Mason University Fairfax VA January 1996

P96-2 Publication List ofMachine Leaming and Inference Lahoratory Part 1 1969middot1987 MLI 96-2 George Mason University Fairfax VA January 1996

1996

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P96-3 Publication List ofMachine uarning and Inference lAboratory Part2 1988-1995 MI196-3 George Mason University Fairfax VA January 1996

P96-4 Kaufman K and Michalski RS A Multistrategy Conceptual Analysis of Economic Data Proceedings ofthe Fourth International Workshop on Artificial Intelligence in Economics and v Management Tel Aviv Israel January 8-10 1996

P96-5 Michalski RS Rosenfeld A Aloimonos Y Duric Z Maloof MA Zhang Q ItProgress On Vision Through Learning A Collaborative Effort of George Mason University and University of Maryland Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-7 Michalski RS Zhang Q Maloof MA and Bloedorn E The MIST Methodology and its Application to Natural Scene Interpretation Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-8 Duric Z Rivlin E and Rosenfeld A Learning an Objects Function by Observing the Object in Action Proceedings of the Image Understanding Workshop Palm Springs CA February 1996

P96-9 Maloof MA Duric Z Michalski RS and Rosenfeld A Recognizing Blasting Caps in X-Ray Images Proceedings of the Image Understanding Workshop Palm Springs CA Feburary 1996

P96-10 Imam JF The AQDT-2 USERS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine Learning and Inference Laboratory MLI 96-4 George Mason University Fairfax VA March 1996

P96-11 ImamIF liThe AQDT-2 PROGRAMMERSS GUIDE A Machine Learning Program for Learning Task-oriented Decision Structures from Decision Rules Reports of the Machine uarning and Inference Laboratory MIl 96-5 George Mason University Fairfax VA March 1996

P96-12 Michalski RS and Wnek J (Eds) Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-251996

P96-13 Alkharouf NW and Michalski RS tlMultistrategy Task-Adaptive Learning Using Dynamically Interlaced Hierarchies A Methodology and Initial Implementation of INTERLACE Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) Harpers Ferry WV May 23-25 1996

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P96-14 Kaufman K Addressing Knowledge Discovery Problems in a Multistrategy Framework Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96) pp 305-312 Harpers Ferry WV May 23-25 1996

P96-15 Bloedorn E and Michalski RS The AQ17-DCI System for Data-Driven Constructive Induction and Its Application to the Analysis of World Economicst1 Proceedings of the 9th International Symposiwn ofIntelligent Systems Zakopane Poland Iune 1996

P96-16 Imam IF and Michalski RS An Empirical Comparison Between Learning Decision Trees from Examples and from Decision Rules Proceedings of the Ninth International Symposiwn on Methodologies for Intelligent Systems (ISMIS-96) Springer Verlag Iune 10shy13 1996

P96-17 Imam IF liDo We Efficiently Estimate the Attributional Relevancy to Learning Systems Proceedings ofthe Ninth International Symposium on Methodologies for Intelligent Systems ISMIS-96 Springer Verlag Iune 10-13 1996

P96-18 Duric Z Fayman IA and Rivlin E Function From Motion IEEE Transactions on Pattern Analysis and Machine Intelligence Vol 18 No6 pp 579-591 Iune 1996

P96-19 Wnek I Kaufman K Bloedorn E and Michalski RS 1nductive Learning System AQ15c The Method and Users Guide Reports of the Machine Learning and Inference Laboratory MLI 96-6 George Mason University Fairfax VA August 1996

P96-20 Bloedorn E Mani I and MacMillan TR Machine Learning of User Profiles Representational Issues II Proceedings ofthe Thirteenth National Conference on AI Portland OR August 4-8 1996

P96-21 Kaufman K and Michalski RS A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Knowledge Discovery System Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining Portland OR August 1996

P96-22 Duric Z and Rosenfeld A Image Sequence Stabilization in Real Time Real-Time Imaging Vol 2 pp 271-284 1996

P96-23 Bloedorn EE Multistrategy Constructive Induction PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-7 George Mason University Fairfax VA 1996

P96-24 Maloof MA and Michalski RS Parial Memory Learning System AQ-PM The Method and Users Guide Reports of the Machine Learning and Inference Laboratory Mil 96-8 George Mason University Fairfax VA 1996

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P96-2S Maloof MA Progressive Parial Memory Learning PhD Dissertation School of Information Technology and Engineering Reports of the Machine Learning and Inference Laboratory MLI 96-8 George Mason University Fairfax VA 1996

P96-26 Lee SW Multistrategy Learning An Empirical Study with AQ + Bayesian Approach Reports of the Machine Learning and Inference Laboratory MLI 96-9 George Mason University Fairfax VA 1996

P96-27 Lee SW WWW-AQWorldWideWebInterface for the AQ Learning System Users and Programmers Guide Reports of the Machine Learning and Inference Laboratory MLI 96shy10 George Mason University Fairfax VA 1996

P96-28 Zhang Q Duric Zand Michalski RS Target detection in SAR images using the MISTAQ method Reports of the Machine Learning and Inference Laboratory MLI 96-12 George Mason University Fairfax VA 1996

1997 P97-1 Publication Ust of the Machine Learning and Inference Laboratory 1988-1997 MLI 97-1 George Mason University Fairfax VA 1997

P97-2 Maloof MA Michalski RS Learning Symbolic Descriptions of Shape for Object

Recognition In X-Ray Images Expert Systems with Applications 12(1 11-20 1997

P97-3 Michalski RS Kaufman KA Data Mining and Knowledge Discovery A Review of Issues and a Multistrategy Approach Reports of the Machine Learning and Inference Laboratory MLI 97-2 George Mason University Fairfax VA 1997

P97-4 Kaufman KA and Michalski RS KGL A Language for Learning II Reports of the Machine Learning and Inference Laboratory MLI 97-3 George Mason University Fairfax VA1997

P97-S Lee S W Fischthal S and Wnek J Using Bayesian Classification for AQ-based Learning with Constructive Induction Reports of the Machine Learning and Inference Laboratory MLI 97-4 George Mason Univesity Fairfax VA 1997

P97-6 Zhang Q and Michalski R S Speeding GA-based Attribute Selection for Image Interpretation Reports ofthe Machine Learning and Inference Laboratory MIl97-S George Mason University Fairfax VA 1997

P97-7 Michalski R S and Imam I F On Learning Decision Structures Fundamenta Matematicae dedicated to the memory of Dr Cecylia Raucher Polish Academy of Sciences 1997 (In press)

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444

P97-8 Michalski R S Bratko I and Kubat M (Eds) Machine Learning and Data Mining Methods and Applications London John Wiley amp Sons 1997(In press)

P97-9 Michalski R S Rosenfeld A Durie Z Maloof M and Zhang Q Applications of Machine Learning in Computer Vision Reports of the Machine Learning and Inference Laboratory MLI 97-6 George Mason University Fairfax VA 1997

P97-10 Lee S W and Michalski R S An Automatic Learning Perfonnance Evaluation Environment ALPE The Method and Userts Guide Reports of the Machine Learning and Inference Laboratory MLI 97-7 George Mason University Fairfax V A 1997

Any questions or requests for papers should be directed to

Research Manager Phone (703) 993-1719 Machine Learning and Inference Laboratory Fax (703) 993-3729 Science amp Tech II Room 413 Email adminaicgmuedu George Mason University Fairfax Virginia 22030-4444