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Seminar abstract

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound

Rong Jin
Associate Professor
Department of Computer Science and Engineering, Michigan State University

Abstract: In this talk, I will present a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve a regression error that is close to the optimal. We also verify the effectiveness of the proposed algorithm by an empirical study.

Bio: Rong Jin focuses his research on statistical machine learning and its application to information retrieval. He has worked on a variety of machine learning algorithms and their application to information retrieval, including retrieval models, collaborative filtering, cross lingual information retrieval, document clustering, and video/image retrieval. He has published over 160 conference and journal articles on related topics. Dr. Jin Ph.D. holds a Ph.D. in Computer Science from Carnegie Mellon University He received the NSF Career Award in 2006.
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