Improved sparse least-squares support vector machines

Cawley, G. C. and Talbot, N. L. C. (2002) Improved sparse least-squares support vector machines. Neurocomputing, 48 (1-4). pp. 1025-1031. ISSN 0925-2312

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Abstract

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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 13 Jun 2011 13:58
Last Modified: 09 Oct 2017 18:26
URI: https://ueaeprints.uea.ac.uk/id/eprint/22158
DOI: 10.1016/S0925-2312(02)00606-9

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