A TUTORİAL ON SUPPORT VECTOR MACHİNES FOR PATTERN RECOGNİTİON

ASLI TAŞÇİ

Christopher J.C. Burges, Data Mining and

Knowledge Discovery 2, 121-167, 1998

OUTLİNE

• Introduction• Linear Support Vector Machines• Nonlinear Support Vector Machines• Limitations• Conclusion

INTRODUCTİON

Classification and Regression tool

Supervised Learning

Linear and non-linear classification performance

APPLİCATİON AREAS

Handwritten Digit Recognition

Object Recognition

Speaker Identification

Text Categorization

Face Detection in Images

LİNEAR SUPPORT VECTOR MACHİNES

Simplest Case: Seperable Data

SVM Equaiton:

Lagranian:

KARUSH-KUHN-TUCKER CONDİTİONS

Constraint optimization

NON-SEPERABLE CASEIntroducing Slack variables for a feasible solution with linear SVM

Lagranian for non-seperable data:

NONLİNEAR SUPPORT VECTOR MACHİNES

Mapping data to a feature space

Example:

Kernel Function:

MERCER’S CONDİTİON

Positive Semi-definite

OPTİMİZATİON PROBLEM

Quadratic programming optimizaiton

TRAİNİNG

Decomposition algorithms for larger problems• Chunking method• Osuna’s decomposition algorithm

LİMİTATİONS

• Choice of the Kernel• Speed• Size• Discrete Data• Multi-class classification

PERFORMANCE OF SVMThe Virtual Support Vector Method• Training the system than creating a

new data by distorting the resulting support vectors.

The reduced set method• Increases the speed of SVM

CONCLUSİON

• New approach to the problem of pattern recognition

• SVM training always find a global minimum• Largely characterized by the choice of its

Kernel

THANK YOU FOR LİSTENİNG