Kernel Methods and Support Vector Machines

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: July 30, 2009,   recorded: June 2009,   views: 17099


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Kernel methods have become a standard tool for pattern analysis during the last fifteen years since the introduction of support vector machines. We will introduce the key ideas and indicate how this approach to pattern analysis enables a relatively easy plug and play application of different tools. The problem of choosing and designing a kernel for specific types of data will also be considered and an overview of different kernels will be given.

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Comment1 andrea, April 5, 2010 at 2:21 p.m.:

the pdf link is broken

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