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Abstract
En 中文
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. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Sparsity
Classification
Generalization error bounds
Statistical learning theory
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Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W