Semi-Supervised Learning

Semi-Supervised Learning

Tobias Scheffer (Humboldt-Universität zu Berlin, Germany)
Copyright: © 2005 |Pages: 6
DOI: 10.4018/978-1-59140-557-3.ch192
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Abstract

For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data.

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