English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Experimentally optimal ν in support vector regression for different noise models and parameter settings

MPS-Authors
/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Chalimourda, A., Schölkopf, B., & Smola, A. (2004). Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural networks, 17(1), 127-141. doi:10.1016/S0893-6080(03)00209-0.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DA2F-E
Abstract
In Support Vector (SV) regression, a parameter ν controls the number of Support Vectors and the number of points that come to lie outside of the so-called var epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression. As a side effect of the experiments, valuable information about the generalization behavior of the remaining SVM parameters and their dependencies is gained. The experimental findings are valid even for complex ‘real-world’ data sets. Based on our results on the role of the ν-SVM parameters, we discuss various model selection methods.