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

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...