A theory of learning from different domains

Volume: 79, Issue: 1, Pages: 151 - 175
Published: May 1, 2010
Abstract
Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work we investigate two questions. First, under what conditions can a classifier trained from...
Paper Details
Title
A theory of learning from different domains
Published Date
May 1, 2010
Volume
79
Issue
1
Pages
151 - 175
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