Robust support vector regression in the primal

DOI10.1016/j.neunet.2008.09.001zbMath1254.68236OpenAlexW2092910747WikidataQ47317446 ScholiaQ47317446MaRDI QIDQ1932127

Yong-Ping Zhao, Jian-Guo Sun

Publication date: 17 January 2013

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2008.09.001

zbMATH Keywords

support vector regressionconcaveconvex procedurenon-convex loss function

Mathematics Subject Classification ID

General nonlinear regression (62J02) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Training robust support vector regression with smooth non-convex loss function ⋮ Parsimonious kernel extreme learning machine in primal via Cholesky factorization ⋮ Robust regularized extreme learning machine for regression with non-convex loss function via DC program ⋮ A Robust Regression Framework with Laplace Kernel-Induced Loss ⋮ Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function

Uses Software

• SVMlight
• LIBSVM
• SVMTorch

Cites Work

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• 10.1162/15324430152733142
• Recursive Finite Newton Algorithm for Support Vector Regression in the Primal
• Robust Truncated Hinge Loss Support Vector Machines
• A finite newton method for classification
• The Concave-Convex Procedure
• Training a Support Vector Machine in the Primal
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines

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