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2004

Sparse LS-SVMs using additive regularization with a penalized validation criterion

8 years 11 months ago
Sparse LS-SVMs using additive regularization with a penalized validation criterion
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which has been recently introduced in [6]. This framework enables computational fusion of training and validation levels and allows to train the model together with finding the regularization constants by solving a single linear system at once. In this paper we show that this framework allows to consider a penalized validation criterion that leads to sparse LS-SVMs. The model, regularization constants and sparseness follow from a convex quadratic program in this case. Regularization has a rich history which dates back to the theory of inverse ill-posed and ill-conditioned problems [12]. Regularized cost functions have been considered e.g. in splines, multilayer perceptrons, regularization networks [7], support vector machines (SVM) and related methods (see e.g. [5]). SVM [13] is a powerful methodology for solving pro...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
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