article

An overview on nonparallel hyperplane support vector machine algorithms

Authors:

Junzhao YuAuthors Info & Claims

Neural Computing and Applications, Volume 25, Issue 5

Pages 975 - 982

https://doi.org/10.1007/s00521-013-1524-6

Published: 01 October 2014 Publication History

Abstract

Support vector machine (SVM) has attracted substantial interest in the community of machine learning. As the extension of SVM, nonparallel hyperplane SVM (NHSVM) classification algorithms have become current researching hot spots in machine learning during the last few years. For binary classification tasks, the idea of NHSVM algorithms is to find a hyperplane for each class, such that each hyperplane is proximal to the data points of one class and far from the data points of the other class. Compared with the classical SVM, NHSVM algorithms have lower computational complexity, work better on XOR problems and can get better generalization performance. This paper reviews three representative NHSVM algorithms, including generalized eigenvalue proximal SVM (GEPSVM), twin SVM (TWSVM) and projection twin SVM (PTSVM), and gives the research progress of them. The aim of this overview is to provide an insightful organization of current developments of NHSVM algorithms, identify their limitations and give suggestions for further research.

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Cited By

Yang YXue ZMa JChang X(2023)Robust projection twin extreme learning machines with capped L 1-norm distance metricNeurocomputing10.1016/j.neucom.2022.09.156517:C(229-242)Online publication date: 14-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.09.156
Shadiani AShoorehdeli M(2023)Online Learning Approach Based on Recursive Formulation for Twin Support Vector Machine and Sparse Pinball Twin Support Vector MachineNeural Processing Letters10.1007/s11063-022-11084-155:4(5143-5165)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11063-022-11084-1
Chen WShao YLi CLiu MWang ZDeng N(2020) ν-projection twin support vector machine for pattern classificationNeurocomputing10.1016/j.neucom.2019.09.069376:C(10-24)Online publication date: 1-Feb-2020
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Index Terms

An overview on nonparallel hyperplane support vector machine algorithms
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 25, Issue 5

October 2014

238 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

Copyright © Copyright © 2014 The Natural Computing Applications Forum.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2014

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Cited By

Yang YXue ZMa JChang X(2023)Robust projection twin extreme learning machines with capped L 1-norm distance metricNeurocomputing10.1016/j.neucom.2022.09.156517:C(229-242)Online publication date: 14-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.09.156
Shadiani AShoorehdeli M(2023)Online Learning Approach Based on Recursive Formulation for Twin Support Vector Machine and Sparse Pinball Twin Support Vector MachineNeural Processing Letters10.1007/s11063-022-11084-155:4(5143-5165)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s11063-022-11084-1
Chen WShao YLi CLiu MWang ZDeng N(2020) ν-projection twin support vector machine for pattern classificationNeurocomputing10.1016/j.neucom.2019.09.069376:C(10-24)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1016/j.neucom.2019.09.069
Li CRen PShao YYe YGuo Y(2020)Generalized elastic net Lp-norm nonparallel support vector machineEngineering Applications of Artificial Intelligence10.1016/j.engappai.2019.10339788:COnline publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1016/j.engappai.2019.103397
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
Singh NSingh PBhagat D(2019)A rule extraction approach from support vector machines for diagnosing hypertension among diabeticsExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.04.029130:C(188-205)Online publication date: 15-Sep-2019
https://dl.acm.org/doi/10.1016/j.eswa.2019.04.029
Yan HYe QYu D(2019)Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterionMachine Language10.1007/s10994-018-5771-8108:6(993-1018)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1007/s10994-018-5771-8
Liu ZLiu HZhao Z(2018)Weighted Least Squares Support Vector Machine for Semi-supervised ClassificationWireless Personal Communications: An International Journal10.5555/3288994.3289077103:1(797-808)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3288994.3289077
Hua XDing S(2018)Weighted least squares projection twin support vector machines with local informationNeurocomputing10.5555/2779626.2779767160:C(228-237)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.5555/2779626.2779767
Yan HYe QZhang TYu DYuan XXu YFu L(2018)Least squares twin bounded support vector machines based on L1-norm distance metric for classificationPattern Recognition10.1016/j.patcog.2017.09.03574:C(434-447)Online publication date: 1-Feb-2018
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