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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

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Zitation

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.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DA2F-E
Zusammenfassung
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.