article

Wavelet twin support vector machine

Authors:

Zhongzhi ShiAuthors Info & Claims

Neural Computing and Applications, Volume 25, Issue 6

Pages 1241 - 1247

https://doi.org/10.1007/s00521-014-1596-y

Published: 01 November 2014 Publication History

Abstract

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly.

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Cited By

Wang LGao CZhao NChen X(2019)A projection wavelet weighted twin support vector regression and its primal solutionApplied Intelligence10.1007/s10489-019-01422-749:8(3061-3081)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s10489-019-01422-7
Zhang RLiu Y(2018)Research on Development and Application of Support Vector Machine - Transformer Fault DiagnosisProceedings of the International Symposium on Big Data and Artificial Intelligence10.1145/3305275.3305328(262-268)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1145/3305275.3305328
Ding SAn YZhang XWu FXue Y(2017)Wavelet twin support vector machines based on glowworm swarm optimizationNeurocomputing10.1016/j.neucom.2016.11.026225:C(157-163)Online publication date: 15-Feb-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.11.026
Show More Cited By

Index Terms

Wavelet twin support vector machine
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. Mathematics of computing
  1. Mathematical analysis

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

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 25, Issue 6

Nov 2014

270 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

Copyright © Copyright © 2014 The Natural Computing Applications Forum.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2014

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Citations

Cited By

Wang LGao CZhao NChen X(2019)A projection wavelet weighted twin support vector regression and its primal solutionApplied Intelligence10.1007/s10489-019-01422-749:8(3061-3081)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s10489-019-01422-7
Zhang RLiu Y(2018)Research on Development and Application of Support Vector Machine - Transformer Fault DiagnosisProceedings of the International Symposium on Big Data and Artificial Intelligence10.1145/3305275.3305328(262-268)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1145/3305275.3305328
Ding SAn YZhang XWu FXue Y(2017)Wavelet twin support vector machines based on glowworm swarm optimizationNeurocomputing10.1016/j.neucom.2016.11.026225:C(157-163)Online publication date: 15-Feb-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.11.026
Zhang XDing SXue Y(2017)An improved multiple birth support vector machine for pattern classificationNeurocomputing10.1016/j.neucom.2016.11.006225:C(119-128)Online publication date: 15-Feb-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.11.006
Ding SZhang JXu XZhang Y(2016)A wavelet extreme learning machineNeural Computing and Applications10.1007/s00521-015-1918-827:4(1033-1040)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1007/s00521-015-1918-8

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