Title: Compactly supported RBF kernels for sparsifying the gram matrix in LS-SVM regression models
Authors: Hamers, Bart ×
Suykens, Johan
De Moor, Bart #
Issue Date: 2002
Publisher: Springer-verlag berlin
Host Document: Artificial neural networks - icann 2002 vol:2415 pages:720-726
Conference: ICANN 2002 location:Madrid, Spain date:Aug. 2002
Abstract: 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.
Description: \emph{Proceedings ICANN 2002}, Madrid, Spain, Aug. 2002
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Electrical Engineering - miscellaneous
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science