Support Vector Machines for Classification and Regression
ISIS Technical Report Support Vector Machines for Classification and Regression Steve Gunn 14 May 1998. Contents 1 Introduction 5. Statistical Learning Theory ........................................ .......................6. VC .... 7. Structural Risk Minimisation ........................................ ........................................ ........... 7. 2 Support Vector Classification 9. The Optimal Separating Hyperplane ........................................ ........9. Linearly Separable Example ........................................ ........................................ .......... 13. The Generalised Optimal Separating Hyperplane .........................14. Linearly Non-Separable Example ........................................ ........................................ .. 16. Generalisation in High Dimensional Feature Space .....................17. Polynomial Mapping 19. 3 Feature Space 21. Kernel Polynomial.
Image Speech and Intelligent Systems Group Hence the hyperplane that optimally separates the data is the one that minimises Φ()ww= 1 2 2. (10) It is independent of b because provided Equation (7) is satisfied (i.e. it is a separating
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