Support Vector and Kernel Methods
John Shawe-Taylor1
1
University of Southampton, School of Electronics and Computer Science,
Southampton, SO17 1BJ, UK
jst@ecs.soton.ac.uk
Abstract
The lectures will introduce the kernel methods approach to pattern analysis
through the particular example of support vector machines for classification.
The presentation touches on: generalization, optimization, dual
representation, kernel design and algorithmic implementations. We then
broaden the discussion to consider general kernel methods by introducing
different kernels, different learning tasks, and subspace methods such as
kernel PCA. The aim is to give a view of the subject that will enable a
newcomer to the field to gain his bearings so that they can move to apply or
develop the techniques for their particular application.
References
Cristianini Nello and Shawe-Taylor John, An Introduction to Support Vector
Machines, Cambridge University Press, 2000.
Shawe-Taylor John and Cristianini Nello, Kernel Methods for Pattern
Analysis, Cambridge University Press, 2004.
Keywords
Support vector machines, kernel methods, statistical learning theory, margin,
pattern analysis, generalization, kernel, string kernel, subspace methods,
principle components analysis, ridge regression, support vectors