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  Embedded methods

Lal, T., Chapelle, O., Weston, J., & Elisseeff, A. (2006). Embedded methods. In I. Guyon, S. Gunn, M. Nikravesh, & L. Zadeh (Eds.), Feature Extraction: Foundations and Applications (pp. 137-165). Berlin, Germany: Springer.

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 Creators:
Lal, TN1, 2, Author              
Chapelle, O1, 2, Author              
Weston, J, Author              
Elisseeff, A, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Embedded methods are a relatively new approach to feature selection. Unlike filter methods, which do not incorporate learning, and wrapper approaches, which can be used with arbitrary classifiers, in embedded methods the features selection part can not be separated from the learning part. Existing embedded methods are reviewed based on a unifying mathematical framework.

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 Dates: 2006
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3012
DOI: 10.1007/978-3-540-35488-8_6
 Degree: -

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Title: Feature Extraction: Foundations and Applications
Source Genre: Book
 Creator(s):
Guyon, I, Editor
Gunn, S, Editor
Nikravesh, M, Editor
Zadeh, LA, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 137 - 165 Identifier: ISBN: 978-3-540-35487-1

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Title: Studies in Fuzziness and Soft Computing
Source Genre: Series
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Pages: - Volume / Issue: 207 Sequence Number: - Start / End Page: - Identifier: -