Transcription of Support Vector Machines for Classification and …
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Support Vector Machines for Classification and …
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 .. 21. Gaussian Radial Basis 21. Exponential Radial Basis Function .. 22. Multi-Layer 22. Fourier Series .. 22. 22. B splines .. 23. Additive Kernels .. 23. Tensor Product 23. Implicit vs. Explicit Bias ..23. Data Normalisation.
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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