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A BOUNDARY METHOD TO SPEED UP TRAINING SUPPORT VECTOR MACHINES

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Computational Methods

Abstract

In this paper, we propose a boundary method to speed up constructing the optimal hyperplane of support vector machines. The boundary, called key vector set, is an approximate small superset of support vector set which is extracted by Parzen window density estimation in the feature space. Experimental results on Checkboard data set show that the proposed method is more efficient than some conventional methods and requires much less memory.

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REFERENCES

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry, Changchun, 130021, China

    Y. Wang, C.G. Zhou, Y.X. Huang & Y.C. Liang

  2. Department of Applied Mathematics, South China University of Technology, Guangzhou, 510640, China

    X.W. Yang

Authors
  1. Y. Wang
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  2. C.G. Zhou
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  3. Y.X. Huang
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  4. Y.C. Liang
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  5. X.W. Yang
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Editor information

Editors and Affiliations

  1. National University of Singapore, Singapore

    G.R. LIU

  2. National University of Singapore, Singapore

    V.B.C. TAN

  3. Hunan University, P.R. China

    X. HAN

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© 2006 Springer

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Wang, Y., Zhou, C., Huang, Y., Liang, Y., Yang, X. (2006). A BOUNDARY METHOD TO SPEED UP TRAINING SUPPORT VECTOR MACHINES. In: LIU, G., TAN, V., HAN, X. (eds) Computational Methods. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3953-9_31

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  • DOI: https://doi.org/10.1007/978-1-4020-3953-9_31

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-3952-2

  • Online ISBN: 978-1-4020-3953-9

  • eBook Packages: EngineeringEngineering (R0)

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