Transcription of Support Vector Machines for Classification and Regression
{{id}} {{{paragraph}}}
Example: bachelor of science
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 .. 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 ..24. Kernel Selection ..24. 4 Classification Example: IRIS data 25. Applications ..28. 5 Support Vector Regression 31. Linear -Insensitive Loss Function .. 32. Quadratic Loss Function .. 33. Huber Loss Function.
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
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: