TSVR: An efficient Twin Support Vector Machine for regression

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...

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Vydané v:	Neural networks Ročník 23; číslo 3; s. 365 - 372
Hlavný autor:	Peng, Xinjun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.04.2010 Elsevier
Predmet:	[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
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