Introduction to Kernel Methods

author: Liva Ralaivola, Aix-Marseille Université
published: Aug. 5, 2010,   recorded: July 2010,   views: 21787


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In this talk, we are going to see the basics of kernels methods. After a brief presentation of a very simple kernel classifier, we'll give the definition of a postive definite kernel and explain Support vector machine learning. Then, a few kernels for structured data, namely sequences and graphs, will be described. The representer theorem is presented, which explains the rationale for the usual kernel expansion encountered when working with kernel methods. Finally, a few elements from statistical learning theory are given.

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Reviews and comments:

Comment1 Alex, May 18, 2011 at 1:39 p.m.:

Brilliant explanation!

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