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by Holger Frhlich, Olivier Chapelle, Bernhard Schlkopf
http://www-ra.informatik.uni-tuebingen.de/mitarb/froehlich/Publikationen/342_froehlich_hf.ps
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Abstract:
The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing crossvalidation and to other existing algorithms for feature selection.
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