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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| Published in | Neural networks Vol. 23; no. 3; pp. 365 - 372 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.04.2010
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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 -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. 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 [epsilon (Porson)]-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. 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 -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.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 -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. 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. |
| Author | Peng, Xinjun |
| Author_xml | – sequence: 1 givenname: Xinjun surname: Peng fullname: Peng, Xinjun email: xjpeng@shnu.edu.cn organization: Department of Mathematics, Shanghai Normal University, 200234, PR China |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22508909$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/19616409$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2009 Elsevier Ltd 2015 INIST-CNRS Copyright 2009 Elsevier Ltd. All rights reserved. Copyright 2009 Elsevier Ltd. All rights reserved. |
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| Keywords | 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 |
| Language | English |
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| SubjectTerms | [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 |
| Title | TSVR: An efficient Twin Support Vector Machine for regression |
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