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. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Cawley, G. C., & Talbot, N. L. C. (2002). A greedy training algorithm for sparse least-squares support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 681–686). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_111
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