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Embedded methods

2006

Book Chapter

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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.

Author(s): Lal, TN. and Chapelle, O. and Weston, J. and Elisseeff, A.
Book Title: Feature Extraction: Foundations and Applications
Pages: 137-165
Year: 2006
Day: 0

Series: Studies in Fuzziness and Soft Computing ; 207
Editors: Guyon, I. , S. Gunn, M. Nikravesh, L. A. Zadeh
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inbook{3012,
  title = {Embedded methods},
  author = {Lal, TN. and Chapelle, O. and Weston, J. and Elisseeff, A.},
  booktitle = {Feature Extraction: Foundations and Applications},
  pages = {137-165},
  series = {Studies in Fuzziness and Soft Computing ; 207},
  editors = {Guyon, I. , S. Gunn, M. Nikravesh, L. A. Zadeh},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  year = {2006}
}