Page History: SVM – Support Vector Machines
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Page Revision: 2010/05/22 17:58
- 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
SVM Related Links
- 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 Machines N. Cristianini and J. Shawe-Taylor Cambridge University Press