article

An overview on twin support vector machines

Authors:

Huajuan HuangAuthors Info & Claims

Artificial Intelligence Review, Volume 42, Issue 2

Pages 245 - 252

https://doi.org/10.1007/s10462-012-9336-0

Published: 01 August 2014 Publication History

Abstract

Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.

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Lyaqini SHadri AEllahyani ANachaoui M(2024)Primal dual algorithm for solving the nonsmooth Twin SVMEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107567128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107567
Hazarika BGupta DBorah P(2023)Fuzzy twin support vector machine based on affinity and class probability for class imbalance learningKnowledge and Information Systems10.1007/s10115-023-01904-865:12(5259-5288)Online publication date: 12-Jun-2023
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Dudzik WNalepa JKawulok M(2021)Evolving data-adaptive support vector machines for binary classificationKnowledge-Based Systems10.1016/j.knosys.2021.107221227:COnline publication date: 5-Sep-2021
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An overview on twin support vector machines

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 42, Issue 2

August 2014

145 pages

ISSN:0269-2821

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Copyright © Copyright © 2014 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2014

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Cited By

Lyaqini SHadri AEllahyani ANachaoui M(2024)Primal dual algorithm for solving the nonsmooth Twin SVMEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107567128:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107567
Hazarika BGupta DBorah P(2023)Fuzzy twin support vector machine based on affinity and class probability for class imbalance learningKnowledge and Information Systems10.1007/s10115-023-01904-865:12(5259-5288)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1007/s10115-023-01904-8
Dudzik WNalepa JKawulok M(2021)Evolving data-adaptive support vector machines for binary classificationKnowledge-Based Systems10.1016/j.knosys.2021.107221227:COnline publication date: 5-Sep-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107221
Chen XXiao Y(2021)Geometric projection twin support vector machine for pattern classificationMultimedia Tools and Applications10.1007/s11042-020-09103-180:15(23073-23089)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11042-020-09103-1
Gao YXie LZhang ZFan Q(2020)Twin support vector machine based on improved artificial fish swarm algorithm with application to flame recognitionApplied Intelligence10.1007/s10489-020-01676-650:8(2312-2327)Online publication date: 3-Mar-2020
https://dl.acm.org/doi/10.1007/s10489-020-01676-6
Ding SSun YAn YJia W(2020)Multiple birth support vector machine based on recurrent neural networksApplied Intelligence10.1007/s10489-020-01655-x50:7(2280-2292)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s10489-020-01655-x
Nasiri JMir A(2020)An enhanced KNN-based twin support vector machine with stable learning rulesNeural Computing and Applications10.1007/s00521-020-04740-x32:16(12949-12969)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1007/s00521-020-04740-x
Rezvani SWang XPourpanah F(2019)Intuitionistic Fuzzy Twin Support Vector MachinesIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2019.289386327:11(2140-2151)Online publication date: 30-Oct-2019
https://dl.acm.org/doi/10.1109/TFUZZ.2019.2893863
Jayadeva Pant HSharma MSoman S(2019)Twin Neural Networks for the classification of large unbalanced datasetsNeurocomputing10.1016/j.neucom.2018.07.089343:C(34-49)Online publication date: 28-May-2019
https://dl.acm.org/doi/10.1016/j.neucom.2018.07.089
Ding SZhao XZhang JZhang XXue Y(2019)A review on multi-class TWSVMArtificial Intelligence Review10.1007/s10462-017-9586-y52:2(775-801)Online publication date: 1-Aug-2019
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