Skip to main content

Cooperative Clustering for Training SVMs

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

Included in the following conference series:

Abstract

Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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.

References

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

    MATH  Google Scholar 

  2. Boser, B., Guyon, I., Vapnik, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: Haussler, D. (ed.) Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)

    Chapter  Google Scholar 

  3. Osuna, E., Freund, R., Girosi, F.: An Improved Training Algorithm for Support Vector Machines. In: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 276–285 (1997)

    Google Scholar 

  4. Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14, Microsoft Research (1998)

    Google Scholar 

  5. Suykens, J.A.K., Vandewalle, J.: Least Square Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293–300 (1999)

    Article  MathSciNet  Google Scholar 

  6. Burges, C.J.C.: Simplified Support Vector Decision Rules. In: Saitta, L. (ed.) Proceedings of 13th International Conference on Machine Learning, San Mateo, CA, pp. 71–77. Morgan Kaufmann Publishers, Inc., San Francisco (1996)

    Google Scholar 

  7. Downs, T., Gates, K.E., Masters, A.: Exact Simplification of Support Vector Solutions. Journal of Machine Learning Research 2, 293–297 (2001)

    Article  Google Scholar 

  8. Lin, K., Lin, C.: A Study on Reduced Support Vector Machines. IEEE Transactions on Neural Networks 14(6), 1449–1459 (2003)

    Article  Google Scholar 

  9. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tian, S., Mu, S., Yin, C. (2006). Cooperative Clustering for Training SVMs. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_141

Download citation

  • DOI: https://doi.org/10.1007/11759966_141

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics