Online Learning with Kernels

author: Yoram Singer, The Hebrew University of Jerusalem
published: Feb. 25, 2007,   recorded: May 2005,   views: 7085
Categories

See Also:

Download slides icon Download slides: Online_Learning_with_Kernels.pdf (155.0┬áKB)


Help icon Streaming Video Help

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.
  Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 59:02
!NOW PLAYING
Watch Part 2
Part 2 24:25
!NOW PLAYING

Description

Online learning is concerned with the task of making decisions on-the-fly as observations are received. We describe and analyze several online learning tasks through the same algorithmic prism. We start with online binary classification and show how to build simple yet efficient and effective online algorithms that incorporate kernel functions. We describe how to analyze the algorithms in the mistake bound model for both separable and inseparable settings. We then describe numerous generalizations of online learning with kernels to other, often more complex, problems. Specifically, we discuss learning algorithms for uniclass prediction, regression, multiclass problems, and sequence prediction. We conclude with discussion on implications to batch learning and generalization. Based on joint works with Koby Crammer, Ofer Dekel, Vineet Gupta, Joseph Keshet, Andrew Ng, Shai Shalev-Shwartz?, Lavi Shpigelman.

Link this page

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

Reviews and comments:

Comment1 Sam, March 13, 2008 at 7:58 p.m.:

It is good talk but the slides are not complete.

Write your own review or comment:

make sure you have javascript enabled or clear this field: