book chapter · IGI Global eBooks · 2005

Semi-Supervised Learning

Cited by 629Abstract and details only

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.

Cite this paper

APA 7

Scheffer, T. (2005). Semi-Supervised Learning. IGI Global eBooks, 1022–1027. https://doi.org/10.4018/978-1-59140-557-3.ch192

MLA 9

Scheffer, Tobias. “Semi-Supervised Learning.” IGI Global eBooks, 2005, pp. 1022–27, https://doi.org/10.4018/978-1-59140-557-3.ch192.

Chicago

Scheffer, Tobias. 2005. “Semi-Supervised Learning.” IGI Global eBooks: 1022–27. https://doi.org/10.4018/978-1-59140-557-3.ch192.

Harvard

Scheffer, T. (2005) ‘Semi-Supervised Learning’, IGI Global eBooks, pp. 1022–1027. Available at: https://doi.org/10.4018/978-1-59140-557-3.ch192.

BibTeX

@incollection{scheffer2005semi,
  title = {{Semi-Supervised Learning}},
  author = {Scheffer, Tobias},
  booktitle = {IGI Global eBooks},
  pages = {1022--1027},
  year = {2005},
  doi = {10.4018/978-1-59140-557-3.ch192}
}

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Semi-Supervised Learning

Abstract

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