Recursive Finite Newton Algorithm for Support Vector Regression in the Primal

DOI10.1162/neco.2007.19.4.1082zbMath1118.68114OpenAlexW2109534047MaRDI QIDQ3440430

Liefeng Bo, Ling Wang, Li-Cheng Jiao

Publication date: 22 May 2007

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

Full work available at URL: https://doi.org/10.1162/neco.2007.19.4.1082

zbMATH Keywords

nonlinear support vector regression

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items

Training robust support vector regression with smooth non-convex loss function ⋮ Robust support vector regression in the primal ⋮ Least absolute deviation support vector regression

Uses Software

• LIBSVM

Cites Work

• Generalized Hessian matrix and second-order optimality conditions for problems with \(C^{1,1}\) data
• Finite algorithms for robust linear regression
• A finite newton method for classification
• Improvements to Platt's SMO Algorithm for SVM Classifier Design
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines