STAT SEMINAR SERIES :: Large margin semi-supervised learning

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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.


  • Workflow Status: Published
  • Created By: Barbara Christopher
  • Created: 10/08/2010
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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