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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, February 21, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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\begin{center}
  \textsl{Junhui Wang}\\
School of Statistics\\
University of Minnesota\\
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\begin{center}
  \textbf{Margin-based Semi-supervised Learning}
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\noindent

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.




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