A greedy training algorithm for sparse least-squares support vector machines

Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, Nicola L. C. (2002) A greedy training algorithm for sparse least-squares support vector machines. In: Artificial Neural Networks — ICANN 2002. Lecture Notes in Computer Science, 2415 . Springer Berlin / Heidelberg, ESP, pp. 681-686. ISBN 978-3-540-44074-1

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Abstract

Suykens et al. [1] describes a form of kernel ridge regression known as the least-squares support vector machine (LS-SVM). In this paper, we present a simple, but efficient, greedy algorithm for constructing near optimal sparse approximations of least-squares support vector machines, in which at each iteration the training pattern minimising the regularised empirical risk is introduced into the kernel expansion. The proposed method demonstrates superior performance when compared with the pruning technique described by Suykens et al. [1], over the motorcycle and Boston housing datasets.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Depositing User:	Vishal Gautam
Date Deposited:	27 Jul 2011 12:32
Last Modified:	22 Apr 2023 02:38
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22161
DOI:	10.1007/3-540-46084-5_111

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