Limitations of kernel and multiple kernel learning

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Oct. 17, 2011,   recorded: September 2011,   views: 5667


Related Open Educational Resources

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.


Many low Vapnik-Chervonenkis (and hence statistically learnable) classes cannot be represented as linear classes in such a way that they can be learnt with large margin approaches. We review these results and then consider recent bounds for multiple kernel learning that suggest large margin methods may be more general applicable.

See Also:

Download slides icon Download slides: simbad2011_shawe_taylor_kernel_01.pdf (224.9┬áKB)

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: