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Cooperative Clustering for Training SVMs

  • Shengfeng Tian
  • Shaomin Mu
  • Chuanhuan Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)

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.

Keywords

Support Vector Machine Support Vector Cluster Center Training Algorithm Regression Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Vapnik, V.N.: Statistical Learning Theory. Join Wiley and Sons, New York (1998)MATHGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 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. 5.
    Suykens, J.A.K., Vandewalle, J.: Least Square Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293–300 (1999)CrossRefMathSciNetGoogle Scholar
  6. 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. 7.
    Downs, T., Gates, K.E., Masters, A.: Exact Simplification of Support Vector Solutions. Journal of Machine Learning Research 2, 293–297 (2001)CrossRefGoogle Scholar
  8. 8.
    Lin, K., Lin, C.: A Study on Reduced Support Vector Machines. IEEE Transactions on Neural Networks 14(6), 1449–1459 (2003)CrossRefGoogle Scholar
  9. 9.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shengfeng Tian
    • 1
  • Shaomin Mu
    • 1
  • Chuanhuan Yin
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingP.R. China

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