STAT SEMINAR SERIES :: Large margin semi-supervised learning

Event Details
  • Date/Time:
    • Friday April 28, 2006
      10:00 am - 12:00 am
  • Location: Executive Room #228
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    N/A
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Contact
Barbara Christopher
Industrial and Systems Engineering
Contact Barbara Christopher
404.385.3102
Summaries

Summary Sentence: STAT SEMINAR SERIES :: Large margin semi-supervised learning

Full Summary: STAT SEMINAR SERIES :: Large margin semi-supervised learning

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.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Status
  • Created By: Barbara Christopher
  • Workflow Status: Published
  • Created On: Oct 8, 2010 - 7:35am
  • Last Updated: Oct 7, 2016 - 9:52pm