By Topic

Semi-supervised Multi-task Learning with Task Regularizations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Fei Wang ; Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA ; Xin Wang ; Tao Li

Multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. There have already been many research efforts on supervised multi-task learning. However, collecting sufficient labeled data for each task is usually time consuming and expensive. In this paper, we consider the semi-supervised multitask learning (SSMTL) problem, where we are given a small portion of labeled points together with a large pool of unlabeled data within each task. We assume that the different tasks can form some task clusters and the task in the same cluster share similar classifier parameters. The final learning problem is relaxed to a convex one and an efficient gradient descent strategy is proposed. Finally the experimental results on both synthetic and real world data sets are presented to show the effectiveness of our method.

Published in:

2009 Ninth IEEE International Conference on Data Mining

Date of Conference:

6-9 Dec. 2009