Training robust support vector regression with smooth non-convex loss function

DOI10.1080/10556788.2011.557725zbMath1248.65067OpenAlexW1985134581MaRDI QIDQ2905346

Ping Zhong

Publication date: 27 August 2012

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2011.557725

zbMATH Keywords

numerical examplesconcave-convex proceduresupport vector regressionNewton-type algorithmconvex functions program

Mathematics Subject Classification ID

Computational learning theory (68Q32) Numerical mathematical programming methods (65K05) Convex programming (90C25) Pattern recognition, speech recognition (68T10)

Related Items

Robust support vector regression with generic quadratic nonconvex \(\varepsilon\)-insensitive loss ⋮ Robust extreme learning machine in the presence of outliers by iterative reweighted algorithm ⋮ Robust regularized extreme learning machine for regression with non-convex loss function via DC program ⋮ DC programming and DCA: thirty years of developments ⋮ A novel semi-supervised support vector machine with asymmetric squared loss ⋮ Low-rank elastic-net regularized multivariate Huber regression model

Uses Software

• LIBSVM

Cites Work

• DC models for spherical separation
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• Robust support vector regression in the primal
• Recursive Finite Newton Algorithm for Support Vector Regression in the Primal
• Robust Truncated Hinge Loss Support Vector Machines
• A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
• A finite newton method for classification
• The Concave-Convex Procedure
• Improvements to Platt's SMO Algorithm for SVM Classifier Design
• Training a Support Vector Machine in the Primal