Learning from Weakly Labeled Data

author: James Kwok, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
published: Aug. 26, 2013,   recorded: July 2013,   views: 5352


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.


In many machine learning problems, the labels of the training examples are incomplete. These include, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. In this talk, focusing on the SVM as the learner, I will describe a label generation strategy that leads to a convex relaxation of the underlying mixed integer programming problem. Computationally, it can be solved via a sequence of SVM subproblems that are much more scalable than other convex SDP relaxations. Empirical results on the three weakly labeled learning tasks above also demonstrate improved performance. (joint work with Yu-Feng Li, Ivor W. Tsang, and Zhi-Hua Zhou)

See Also:

Download slides icon Download slides: roks2013_kwok_data_01.pdf (2.4┬áMB)

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: