EVENTS
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STAT SEMINAR SERIES :: Large margin semi-supervised learning
GUEST LECTURER
Xiaotong Shen
AFFILIATION
University of Minnesota
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 this talk, I will discuss how to combine unlabeled and labeled data to enhance the generalization accuracy of classification. A large margin technique will be presented, which utilizes grouping information from unlabeled data, together with the concept of margins, in a form of regularization controlling the interplay between labeled and unlabeled data. Computational aspects will be discussed through difference convex programming, in addition to a tuning method that involves both labeled and unlabeled data, for tuning in regularization. Finally, numerical examples will be provided.
This work is joint with Junhui Wang.
DATE & TIME
Friday, April 28, 2006 -- 10:00 AM
DURATION
1 hour
LOCATION
Executive Room #228
CONTACT PERSON
Ming Yuan
