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Exact simplification of support vector solutions

Authors:

Tom Downs,

Kevin E. Gates,

Annette MastersAuthors Info & Claims

The Journal of Machine Learning Research, Volume 2

Pages 293 - 297

Published: 01 March 2002 Publication History

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Abstract

This paper demonstrates that standard algorithms for training support vector machines generally produce solutions with a greater number of support vectors than are strictly necessary. An algorithm is presented that allows unnecessary support vectors to be recognized and eliminated while leaving the solution otherwise unchanged. The algorithm is applied to a variety of benchmark data sets (for both classification and regression) and in most cases the procedure leads to a reduction in the number of support vectors. In some cases the reduction is substantial.

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Cited By

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Akbari MKhorashadizadeh SMajidi M(2022)Support vector machine classification using semi-parametric modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07376-226:19(10049-10062)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00500-022-07376-2
Jayadeva Sharma MSoman SPant H(2018)Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature SpaceNeural Processing Letters10.5555/3288065.328813548:2(881-913)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288065.3288135
Nakahara HYonekawa HFujii TSato SAnderson JBazargan K(2018)A Lightweight YOLOv2Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays10.1145/3174243.3174266(31-40)Online publication date: 15-Feb-2018
https://dl.acm.org/doi/10.1145/3174243.3174266
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Exact simplification of support vector solutions
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 2, Issue

3/1/2002

735 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

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Published: 01 March 2002

Published in JMLR Volume 2

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Akbari MKhorashadizadeh SMajidi M(2022)Support vector machine classification using semi-parametric modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07376-226:19(10049-10062)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00500-022-07376-2
Jayadeva Sharma MSoman SPant H(2018)Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature SpaceNeural Processing Letters10.5555/3288065.328813548:2(881-913)Online publication date: 1-Oct-2018
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Nakahara HYonekawa HFujii TSato SAnderson JBazargan K(2018)A Lightweight YOLOv2Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays10.1145/3174243.3174266(31-40)Online publication date: 15-Feb-2018
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Peng JRafferty KFerguson S(2017)A fast algorithm for sparse support vector machines for mobile computing applicationsNeurocomputing10.1016/j.neucom.2015.11.136230:C(160-172)Online publication date: 22-Mar-2017
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Ring MEskofier B(2016)An approximation of the Gaussian RBF kernel for efficient classification with SVMsPattern Recognition Letters10.1016/j.patrec.2016.08.01384:C(107-113)Online publication date: 1-Dec-2016
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