PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Sparsity Driven Kernel Machine Based on Minimizing a Generalization Error Bound
Dori Peleg and Ron Meir
Pattern Recognition Volume In press, 2008.

Abstract

A new sparsity driven kernel classifier is presented based on the minimization of a recently derived data-dependent generalization error bound. The objective function consists of the usual hinge loss function penalizing training errors and a concave penalty function of the expansion coefficients. The problem of minimizing the non-convex bound is addressed by a successive linearization approach, whereby the problem is transformed into a sequence of linear programs. The algorithm produced comparable error rates to the standard Support Vector Machine but significantly reduced the number of support vectors and the concomitant classification time.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5248
Deposited By:Ron Meir
Deposited On:24 March 2009