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Embedded methods

MPG-Autoren
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Lal,  TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Elisseeff,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Lal, T., Chapelle, O., Weston, J., & Elisseeff, A. (2006). Embedded methods. In Feature Extraction: Foundations and Applications (pp. 137-165). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D32F-A
Zusammenfassung
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.