Abstract

We develop an approach for feature elimination in support vector machines (and empirical risk minimization), based on recursive elimination of features. We present theoretical properties of this method and show that this is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present case studies to show that the assumptions are met in most practical situations and also present simulation studies to demonstrate performance of the proposed approach.

Disciplines

Biostatistics

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Biostatistics Commons

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