SVM – Support Vector Machines
Modified on 2010/05/22 18:00 by Administrator — Categorized as: Data Mining
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