introduction
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Introduction. Mohammad Beigi Department of Biomedical Engineering Isfahan University [email protected]. Pattern recognition and Machine Learning. Syllabus Introduction, Linear Models for classification Neural Networks (MLP, RBF, SOM, LVQ, ADALINE) - PowerPoint PPT PresentationTRANSCRIPT
Pattern recognition and Machine Learning
Syllabus Introduction,Linear Models for classificationNeural Networks (MLP, RBF, SOM, LVQ, ADALINE) Kernel Methods & Support Vector Machines Statistical Pattern Recognition ? (HMM,EM, Clustering and unsupervised learning ? Feature Selection and Dimension reduction ?
Pattern recognition and Machine Learning
TextsR. O. Duda, P. E. Hart, D. G. Stork,
Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.
M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
• Midterm 25%• Final 40%• Computer assignments 10%• Final Programming Project 15%• Seminar 10%
Evaluation
Human Perception
Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe, e.g
Understanding spoken wordsreading handwritingdistinguishing fresh food from its smell
We would like to give similar capabilities to machines
What is Pattern Recognition? A pattern is an entity, vaguely defined, that could be given a name, e.g.,
fingerprint image, handwritten word, human face, speech signal, DNA sequence,
Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest, make sound and reasonable decisions about the categories of the
patterns.
Human and Machine Perception
We are often influenced by the knowledge of how patterns are modeled and recognized in nature when we develop pattern recognition algorithms. Research on machine perception also helps us gain deeper understanding and appreciation for pattern recognition systems in nature. Yet, we also apply many techniques that are purelynumerical and do not have any correspondence in naturalsystems.
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Pattern Recognition Applications
Figure 9: Clustering of Microarray Data
Pattern Recognition Applications
Figure 10: Brain Control Interface
Regression: Polynomial Curve Fitting
t is continuous
Sum-of-Squares Error Function
* min ( )w Arg E w Optimization Problem
0th Order Polynomial
1st Order Polynomial
3rd Order Polynomial
9th Order Polynomial
Over-fitting
Root-Mean-Square (RMS) Error:
Polynomial Coefficients
Data Set Size: 9th Order Polynomial
Data Set Size: 9th Order Polynomial
Regularization ;ridge regression
Penalize large coefficient values
Shrinkage: reduce the order of method
~* min ( )w Arg E w
Regularization:
Regularization:
Regularization: vs.
Polynomial Coefficients
Optimization Problem: Finding optimum ,M
Classification example: Handwritten Digit Recognition
28*28 Pixel image : 784 real numbers, training set: 1{ ,.... }Nx x1x
( ),y x t {1,..,9}t
Pattern recognition approaches
Statistical Pattern recognition
Statistical Pattern recognition
Structural Pattern Recognition
Neural Pattern Recognition