Holger Fröhlich and Andreas Zell
Feature Subset Selection for Support Vector Machines by Incremental
Regularized Risk Minimization
Abstract
In this paper we present a novel feature selection algorithm for SVMs
which works by decreasing the regularized risk in an iterative manner by using
a combination of a backward elimination procedure together with an exchange
algorithm. It is applicable to linear as well as to nonlinear problems.
We test this new algorithm
on toy and real life data sets and show its good performance in comparision to state-of-the-art feature selection methods.
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BibTeX
@conference{Froe04,
AUTHOR="H. Fröhlich and A. Zell",
TITLE="{Feature Subset Selection for Support Vector Machines by Incremental Regularized Risk Minimization}",
ORGANIZATION="IEEE Int. Joint Conf. on Neural Networks (IJCNN)",
VOLUME 3,
PAGES="2041 - 2046",
YEAR=2004
}
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