Feature Selection via Concave Minimization and Support Vector Machines

Abstract

Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of… (More)

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@inproceedings{Bradley1998FeatureSV, title={Feature Selection via Concave Minimization and Support Vector Machines}, author={Paul S. Bradley and Olvi L. Mangasarian}, booktitle={ICML}, year={1998} }