Tuning of the hyperparameters for \(L2\)-loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique
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Publication:1669630
DOI10.1016/j.patcog.2015.06.017zbMath1394.68276OpenAlexW1194762648MaRDI QIDQ1669630
Shen-Huan Chou, Chin-Chun Chang
Publication date: 3 September 2018
Published in: Pattern Recognition (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.patcog.2015.06.017
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)
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- Bayesian Trigonometric Support Vector Classifier
- 10.1162/153244303322753706
- Choosing multiple parameters for support vector machines
- Gene selection for cancer classification using support vector machines
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