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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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

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
Depositing User: Vishal Gautam
Date Deposited: 27 Jul 2011 12:32
Last Modified: 26 Sep 2022 00:11
URI: https://ueaeprints.uea.ac.uk/id/eprint/22161
DOI: 10.1007/3-540-46084-5_111

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