Skip to main content

Increasing the Efficiency of Support Vector Machine by Simplifying the Shape of Separation Hypersurface

  • Conference paper
Computational and Information Science (CIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3314))

Included in the following conference series:

Abstract

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM) by simplifying the shape of separation hypersurface. First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set. Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)

    MATH  Google Scholar 

  2. Zhan, Y., Shen, D.: Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 688–696. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  3. Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)

    Article  Google Scholar 

  4. Osuna, E., Girosi, F.: Reducing the run-time complexity of Support Vector Machines. In: ICPR, Brisbane, Australia (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhan, Y., Shen, D. (2004). Increasing the Efficiency of Support Vector Machine by Simplifying the Shape of Separation Hypersurface. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_114

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30497-5_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24127-0

  • Online ISBN: 978-3-540-30497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics