Asymmetric least squares support vector machine classifiers

DOI10.1016/J.CSDA.2013.09.015zbMath1471.62092OpenAlexW2070902746MaRDI QIDQ1615251

R. Smith

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/418651

zbMATH Keywords

classificationsupport vector machineasymmetric least squaresleast squares support vector machine

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

An SVM-like approach for expectile regression ⋮ Iteratively reweighted least square for asymmetric L₂-Loss support vector regression ⋮ On Lagrangian L2-norm pinball twin bounded support vector machine via unconstrained convex minimization ⋮ A novel semi-supervised support vector machine with asymmetric squared loss ⋮ A majorization-minimization scheme forL₂support vector regression

Uses Software

• UCI-ml
• LS-SVMlab
• SVM

Cites Work

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• Geoadditive expectile regression
• Evolution strategies based adaptive \(L_{p}\) LS-SVM
• Estimating conditional quantiles with the help of the pinball loss
• Optimal expectile smoothing
• Robustness of reweighted least squares kernel based regression
• Weighted least squares support vector machines: robustness and sparse approximation
• Quantiles, expectiles and splines
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• 10.1162/1532443041827925

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