Second Order Optimization of Kernel Parameters

presenter: Francis R. Bach, INRIA - SIERRA project-team
published: Dec. 20, 2008,   recorded: December 2008,   views: 4593
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

We investigate the use of second order optimization approaches for solving the multiple kernel learning (MKL) problem. We show that the hessian of the MKL can be computed efficiently and this information can be used to compute a better descent direction than the gradient (used in the state-of-the-art SimpleMKL algorithm). We then empirically show that our new approaches outperforms SimpleMKL in terms of computational efficiency.

See Also:

Download slides icon Download slides: lkasok08_chapelle_soook_01.pdf (165.5 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: