Universal Learning over Related Distributions and Adaptive Graph Transduction

author: Wei Fan, Baidu, Inc.
published: Oct. 20, 2009,   recorded: September 2009,   views: 2964


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.


The basis assumption “training and test data drawn from the same distribution” is often violated. We propose one common solution to cover various scenarios of learning under “different but related distributions” in a single framework. Examples include (a) sample selection bias (b) transfer learning and (c) uncertain training data. The main motivation is that one could ideally solve as many problems as possible with a single approach. The proposed solution extends graph transduction using the maximum margin principle over unlabeled data. The error of the proposed method is bounded even when the training and testing distributions are different. Experiment results demonstrate that the proposed method improves the traditional graph transduction by as much as 15% in accuracy and AUC in all common situations of distribution difference. Most importantly, it outperforms, by up to 10% in accuracy, several state-of-art approaches proposed to solve specific category of distribution difference.

See Also:

Download slides icon Download slides: ecmlpkdd09_fan_ulrdagt_01.pdf (631.6 KB)

Download slides icon Download slides: ecmlpkdd09_fan_ulrdagt_01.ppt (1.6 MB)

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: