Title: A comparison of pruning algorithms for sparse least squares support vector machines
Authors: Hoegaerts, Luc ×
Suykens, Johan
Vandewalle, Joos
De Moor, Bart #
Issue Date: 2004
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:3316 pages:1247-1253
Conference: International Conference on Neural information processing (ICONIP 2004) edition:11th location:Calcutta, India date:Nov. 2004
Abstract: Least Squares Support Vector Machines (LS-SVM) is a proven method for classification and function approximation. In comparison to the standard Support Vector Machines (SVM) it only requires solving a linear system, but it lacks sparseness in the number of solution terms. Pruning can therefore be applied. Standard ways of pruning the LS-SVM consist of recursively solving the approximation problem and subsequently omitting data that have a small error in the previous pass and are based on support values. We suggest a slightly adapted variant that improves the performance significantly. We assess the relative regression performance of these pruning schemes in a comparison with two (for pruning adapted) subset selection schemes, -one based on the QR decomposition (supervised), one that searches the most representative feature vector span (unsupervised)-, random omission and backward selection on independent test sets in some benchmark experiments(1).
Description: \emph{Proceedings of the 11th International Conference on Neural Information Processing (ICONIP 2004)}, Calcutta, India, Nov. 2004
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Electrical Engineering - miscellaneous
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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