TSVR: An efficient Twin Support Vector Machine for regression

• Published in: Neural networks Vol. 23; no. 3; pp. 365 - 372
• Main Author: Peng, Xinjun
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.04.2010; Elsevier
• Subjects: [formula omitted]-insensitive bound; Algorithms; Applied sciences; Artificial Intelligence; Computer science; control theory; systems; Computer Simulation; Data processing. List processing. Character string processing; Databases, Factual; Exact sciences and technology; Least-Squares Analysis; Machine learning; Memory organisation. Data processing; Nonparallel planes; Regression; Regression Analysis; Software; Support vector machine; Time Factors; Nonparallel planes; Regression; ϵ -insensitive bound; Support vector machine; Machine learning; Statistical analysis; Minimization; Regression analysis; Neural network; insensitive bound; Inequality constraint; Vector support machine; Convex function; Artificial intelligence; Quadratic function
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2009.07.002

The learning speed of classical Support Vector Regression (SVR) is low, since it is constructed based on the minimization of a convex quadratic function subject to the pair groups of linear inequality constraints for all training samples. In this paper we propose Twin Support Vector Regression (TSVR), a novel regressor that determines a pair of ϵ -insensitive up- and down-bound functions by solving two related SVM-type problems, each of which is smaller than that in a classical SVR. The TSVR formulation is in the spirit of Twin Support Vector Machine (TSVM) via two nonparallel planes. The experimental results on several artificial and benchmark datasets indicate that the proposed TSVR is not only fast, but also shows good generalization performance.