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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Veröffentlicht in:	Neural networks Jg. 23; H. 3; S. 365 - 372
1. Verfasser:	Peng, Xinjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.04.2010 Elsevier
Schlagworte:	[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
Online-Zugang:	Volltext
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