Experimentally optimal v in support vector regression for different noise models and parameter settings
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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.
| Author(s): | Chalimourda, A. and Schölkopf, B. and Smola, AJ. |
| Links: | |
| Journal: | Neural Networks |
| Volume: | 17 |
| Number (issue): | 1 |
| Pages: | 127-141 |
| Year: | 2004 |
| Month: | January |
| Day: | 0 |
| BibTeX Type: | Article (article) |
| DOI: | 10.1016/S0893-6080(03)00209-0 |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@article{4680,
title = {Experimentally optimal v in support vector regression for different noise models and parameter settings},
journal = {Neural Networks},
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.},
volume = {17},
number = {1},
pages = {127-141},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jan,
year = {2004},
author = {Chalimourda, A. and Sch{\"o}lkopf, B. and Smola, AJ.},
doi = {10.1016/S0893-6080(03)00209-0},
month_numeric = {1}
}