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- Linear Support Vector Machine

Given a set of points with label

The SVM finds a hyperplane defined by the pair (w,b)

(where w is the normal to the plane and b is the distance from the origin)

s.t.

- What if the data is not linearly separable?

- Project the data to high dimensional space where it is linearly separable and then we can use linear SVM – (Using Kernels)

Classification using SVM (w,b)

In non linear case we can see this as

Kernel – Can be thought of as doing dot product in some high dimensional space

Example of Non-linear SVM

SVM

- Relatively new concept
- Nice Generalization properties
- Hard to learn – learned in batch mode using quadratic programming techniques
- Using kernels can learn very complex functions

- http://svm.dcs.rhbnc.ac.uk/
- http://www.kernel-machines.org/
- C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
- SVM light – Software (in C) http://ais.gmd.de/~thorsten/svm_light
- BOOK: An Introduction to Support Vector MachinesN. Cristianini and J. Shawe-TaylorCambridge University Press