Student Seminar Series - June 22, 2006
University of Minnesota
School of Statistics
College of Liberal Arts
Large
Margin Semi-supervised Learning
Junhui
Wang
Thursday, June 22, 2006
10:00 AM, 110
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 9:30 AM
300 Ford Hall
Abstract
In classification, semi-supervised learning occurs when a large amount
of unlabeled data is available with only a small number of labeled
data. In such a situation, how to enhance predictability of
classification through unlabeled data is the focus. In this talk, I
will present a novel margin-based semi-supervised learning methodology,
utilizing grouping information from unlabeled data, together with the
concept of margins, in a form of regularization controlling the
interplay between labeled and unlabeled data. In particular, I will
discuss three aspects: (1) the idea and methodology development; (2)
computational tools; (3) a statistical learning theory. Numerical
examples will be provided to demonstrate the advantage of our proposed
methodology, particularly against SVM with labeled data alone as well
as transductive SVM.