Support Vector and Kernel Methods
published: Feb. 25, 2007, recorded: July 2005, views: 2492
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !