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CIKM
2009
Springer

Large margin transductive transfer learning

9 years 1 months ago
Large margin transductive transfer learning
Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. We have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the Support Vector Machine (SVM) paradigm, of a large margin hyperplane classifier in a feature space. We show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines. Cate...
Brian Quanz, Jun Huan
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Brian Quanz, Jun Huan
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