Experimentally optimal \(\nu\) in support vector regression for different noise models and parameter settings

DOI10.1016/S0893-6080(03)00209-0zbMath1072.68541OpenAlexW2041099987WikidataQ47303084 ScholiaQ47303084MaRDI QIDQ1887132

Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola

Publication date: 23 November 2004

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0893-6080(03)00209-0

zbMATH Keywords

Model selectionGaussian kernel\(\nu\)-Support Vector machinesOptimal \(\nu\)Risk minimizationSupport Vector machine parametersSupport Vector machinesSupport Vector regression

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

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