Compactly supported RBF kernels for sparsifying the gram matrix in LS-SVM regression models
Bart Hamers, Johan A. K. Suykens, Bart De Moor · 2002
In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels recently proposed by Genton may lead to computational improvements and memory reduction. Examples however illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance for some applications, e.g. in chaotic time-series prediction. As a result, the usefulness of such kernels may be much more application dependent than the use of the RBF kernel.