Selection of meta-parameters for support vector regression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

57 Scopus citations


We propose practical recommendations for selecting metaparameters for SVM regression (that is, ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than resampling approaches commonly used in SVM applications. Good generalization performance of the proposed parameter selection is demonstrated empirically using several lowdimensional and high-dimensional regression problems. In addition, we compare generalization performance of SVM regression (with proposed choiceε) with robust regression using 'least-modulus' loss function (ε=0). These comparisons indicate superior generalization performance of SVM regression.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks, ICANN 2002 - International Conference, Proceedings
EditorsJose R. Dorronsoro, Jose R. Dorronsoro
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783540440741
StatePublished - 2002
Event2002 International Conference on Artificial Neural Networks, ICANN 2002 - Madrid, Spain
Duration: Aug 28 2002Aug 30 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2415 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2002 International Conference on Artificial Neural Networks, ICANN 2002

Fingerprint Dive into the research topics of 'Selection of meta-parameters for support vector regression'. Together they form a unique fingerprint.

Cite this