1. A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION ASLI TAŞÇI Christopher J.C. Burges, Data Mining and Knowledge Discovery 2, 121-167, 1998

2. OUTLINE Introduction Linear Support Vector Machines Nonlinear Support Vector Machines Limitations Conclusion

3. INTRODUCTION Classification and Regression tool Supervised Learning Linear and non-linear classification performance

4. APPLICATION AREAS Handwritten Digit Recognition Object Recognition Speaker Identification Text Categorization Face Detection in Images

5. LINEAR SUPPORT VECTOR MACHINES Simplest Case: Seperable Data SVM Equaiton: Lagranian:

6. KARUSH-KUHN-TUCKER CONDITIONS Constraint optimization

7. NON-SEPERABLE CASE Introducing Slack variables for a feasible solution with linear SVM Lagranian for non-seperable data:

8. NONLINEAR SUPPORT VECTOR MACHINES Mapping data to a feature space Example: Kernel Function:

9. MERCER’S CONDITION Positive Semi-definite

10. OPTIMIZATION PROBLEM Quadratic programming optimizaiton

11. TRAINING Decomposition algorithms for larger problems Chunking method Osuna’s decomposition algorithm

12. LIMITATIONS Choice of the Kernel Speed Size Discrete Data Multi-class classification

13. PERFORMANCE OF SVM The 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

14. CONCLUSION New approach to the problem of pattern recognition SVM training always find a global minimum Largely characterized by the choice of its Kernel

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