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Combining Labeled and Unlabeled Data with Co-trainingby: Avrim Blum, Tom Mitchell
In COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers (1998)
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AbstractWe consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks that point to...
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