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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Vydáno v:	Neural networks Ročník 23; číslo 3; s. 365 - 372
Hlavní autor:	Peng, Xinjun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.04.2010 Elsevier
Témata:	[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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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0893-6080 1879-2782 1879-2782
DOI:	10.1016/j.neunet.2009.07.002