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Semi-Supervised Learning
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Author(s): Tobias Scheffer (Humboldt-Universität zu Berlin, Germany)
Copyright: 2005
Pages: 6
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
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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