Improved sparse least-squares support vector machines

Gavin C. Cawley

doi:10.1016/S0925-2312(02)00606-9

2002-10-01

PRE

Gavin C. Cawley *

Nicola L. C. Talbot

DOI：10.1016/S0925-2312(02)00606-9

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摘要

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Suykens et al. (Neurocomputing (2002), in press) describe a weighted least-squares formulation of the support vector machine for regression problems and present a simple algorithm for sparse approximation of the typically fully dense kernel expansions obtained using this method. In this paper, we present an improved method for achieving sparsity in least-squares support vector machines, which takes into account the residuals for all training patterns, rather than only those incorporated in the sparse kernel expansion. The superiority of this algorithm is demonstrated on the motorcycle and Boston housing data sets. (C) 2002 Elsevier Science B.V. All rights reserved.

Keyword:

support vector machines

sparse approximation

kernel ridge regression