Support Vector and Kernel Methods

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Feb. 25, 2007,   recorded: July 2005,   views: 18100

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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.

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