Tuning of the hyperparameters for \(L2\)-loss SVMs with the RBF kernel by the maximum-margin principle and the jackknife technique

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

zbMATH Keywords

jackknife method\(L\)2-loss support vector machinesmaximum-margin principlesRBF kernels

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Choosing shape parameters for regression in reproducing kernel Hilbert space and variable selection

Uses Software

• UCI-ml
• FLANN
• LMNN

Cites Work

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• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Gaussian kernel optimization for pattern classification
• Bayesian approach to feature selection and parameter tuning for support vector machine classifiers
• Radius Margin Bounds for Support Vector Machines with the RBF Kernel
• 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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