JSM 2005 - Toronto

Abstract #302408

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 298
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #302408
Title: Learning Functional Structures from Multiple Tasks and Unlabeled Data
Author(s): Tong Zhang*+ and Rie Ando
Companies: IBM and IBM
Address: Yorktown Heights, NY, NY, 10598, USA
Keywords: semi-supervised learning
Abstract:

In this talk, we consider the problem of learning good structures on hypothesis spaces (that is, what good classifiers are like) from multiple learning tasks. A general framework is presented in which the structural learning problem can be formulated and analyzed theoretically. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. We will show it is possible to create tasks for learning good predictive structures from unlabeled data. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting.

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Revised March 2005