Various Formulations for Learning the Kernel and Structured Sparsity

author: Massimiliano Pontil, Department of Computer Science, University College London
published: Jan. 12, 2011,   recorded: December 2010,   views: 4171
Categories

Slides

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

I will review an approach to learning the kernel, which consists in minimizing a convex objective function over a prescribed set of kernel matrices. I will establish some important properties of this problem and present a reformulation of it from a feature space perspective. A well studied example covered by this setting is multiple kernel learning, in which the set of kernels is the convex hull of a finite set of basic kernels. I will discuss extensions of this setting to more complex kernel families, which involve additional constraints and a continuous parametrization. Some of these examples are motivated by multi-task learning and structured sparsity, which I will describe in some detail during the talk.

See Also:

Download slides icon Download slides: nipsworkshops2010_pontil_vfl_01.pdf (328.7 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: