HYPER-PARAMETER SELECTION FOR SPARSE LS-SVM VIA MINIMIZATION OF ITS LOCALIZED GENERALIZATION ERROR

DOI10.1142/S0219691313500306zbMath1270.68253OpenAlexW2152721295MaRDI QIDQ2846508

Patrick P. K. Chan, Daniel S. Yeung, Wing W. Y. Ng, Binbin Sun

Publication date: 5 September 2013

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691313500306

zbMATH Keywords

sparsityleast squares support vector machine (LS-SVM)sensitivity measurehyper-parameter selectionlocalized generalization error model (L-GEM)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Cites Work

• Efficient cross-validation for kernelized least-squares regression with sparse basis expansions
• Evolution strategies based adaptive \(L_{p}\) LS-SVM
• Benchmarking least squares support vector machine classifiers
• Feature selection using localized generalization error for supervised classification problems using RBFNN
• Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers.
• Sparse kernel learning with LASSO and Bayesian inference algorithm

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