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Letters: Reduced twin support vector regression

Authors:

Suresh ChandraAuthors Info & Claims

Neurocomputing, Volume 74, Issue 9

Pages 1474 - 1477

https://doi.org/10.1016/j.neucom.2010.11.003

Published: 01 April 2011 Publication History

Abstract

We propose the reduced twin support vector regressor (RTSVR) that uses the notion of rectangular kernels to obtain significant improvements in execution time over the twin support vector regressor (TSVR), thus facilitating its application to larger sized datasets.

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Cited By

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Gu BCao JPan FXiong W(2023)Incremental learning for Lagrangian ε-twin support vector regressionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07755-927:9(5357-5375)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00500-022-07755-9
Peng XXu DKong LChen D(2019)L1-norm loss based twin support vector machine for data recognitionInformation Sciences: an International Journal10.1016/j.ins.2016.01.023340:C(86-103)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2016.01.023
Gupta UGupta D(2019)An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss functionApplied Intelligence10.1007/s10489-019-01465-w49:10(3606-3627)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10489-019-01465-w
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Published In

Neurocomputing Volume 74, Issue 9

April, 2011

203 pages

ISSN:0925-2312

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Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2011

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Gu BCao JPan FXiong W(2023)Incremental learning for Lagrangian ε-twin support vector regressionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07755-927:9(5357-5375)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00500-022-07755-9
Peng XXu DKong LChen D(2019)L1-norm loss based twin support vector machine for data recognitionInformation Sciences: an International Journal10.1016/j.ins.2016.01.023340:C(86-103)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2016.01.023
Gupta UGupta D(2019)An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss functionApplied Intelligence10.1007/s10489-019-01465-w49:10(3606-3627)Online publication date: 1-Oct-2019
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Lpez JMaldonado S(2018)Robust twin support vector regression via second-order cone programmingKnowledge-Based Systems10.1016/j.knosys.2018.04.005152:C(83-93)Online publication date: 15-Jul-2018
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Xu YLi XPan XYang Z(2018)Asymmetric ?-twin support vector regressionNeural Computing and Applications10.1007/s00521-017-2966-z30:12(3799-3814)Online publication date: 1-Dec-2018
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Ding SHua XYu J(2018)An overview on nonparallel hyperplane support vector machine algorithmsNeural Computing and Applications10.1007/s00521-013-1524-625:5(975-982)Online publication date: 27-Dec-2018
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Hao P(2017)Pairing support vector algorithm for data regressionNeurocomputing10.1016/j.neucom.2016.11.024225:C(174-187)Online publication date: 15-Feb-2017
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Parastalooi NAmiri AAliheidari P(2016)Modified twin support vector regressionNeurocomputing10.1016/j.neucom.2016.01.105211:C(84-97)Online publication date: 26-Oct-2016
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Letters: Training support vector machines based on stacked generalization for image classification

Letters: Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers

A comparison of reduced support vector machines

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