Training Data Selection for Support Vector Machines

  • Jigang Wang
  • Predrag Neskovic
  • Leon N. Cooper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3610)


In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained quadratic programming problem, which requires large memory and enormous amounts of training time for large-scale problems. In contrast, the SVM decision function is fully determined by a small subset of the training data, called support vectors. Therefore, it is desirable to remove from the training set the data that is irrelevant to the final decision function. In this paper we propose two new methods that select a subset of data for SVM training. Using real-world datasets, we compare the effectiveness of the proposed data selection strategies in terms of their ability to reduce the training set size while maintaining the generalization performance of the resulting SVM classifiers. Our experimental results show that a significant amount of training data can be removed by our proposed methods without degrading the performance of the resulting SVM classifiers.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boser, B.E., Guyon, I.M., 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 (1992)Google Scholar
  2. 2.
    Cortes, C., Vapnik, V.N.: Support vector networks. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  3. 3.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATHGoogle Scholar
  4. 4.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    Shin, H.J., Cho, S.Z.: Fast pattern selection for support vector classifiers. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, pp. 376–387. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Almeida, M.B., Braga, A.P., Braga, J.P.: SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: Proceedings of the 6th Brazilian Symposium on Neural Networks, pp. 162–167 (2000)Google Scholar
  7. 7.
    Zheng, S.F., Lu, X.F., Zheng, N.N., Xu, W.P.: Unsupervised clustering based reduced support vector machines. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. 821–824 (2003)Google Scholar
  8. 8.
    Koggalage, R., Halgamuge, S.: Reducing the number of training samples for fast support vector machine classification. Neural Information Processing - Letters and Reviews 2(3), 57–65 (2004)Google Scholar
  9. 9.
    Zhang, W., King, I.: Locating support vectors via β-skeleton technique. In: Proceedings of the International Conference on Neural Information Processing (ICONIP), pp. 1423–1427 (2002)Google Scholar
  10. 10.
    Abe, S., Inoue, T.: Fast training of support vector machines by extracting boundary data. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 308–313. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Lee, Y.J., Mangasarian, O.L.: RSVM: Reduced support vector machines. In: Proceedings of the First SIAM International Conference on Data Mining (2001)Google Scholar
  12. 12.
    Huang, S.Y., Lee, Y.J.: Reduced support vector machines: a statistical theory. Technical report, Institute of Statistical Science, Academia Sinica, Taiwan (2004),
  13. 13.
    Vapnik, V.N.: Estimation of Dependence Based on Empirical Data. Springer, Berlin (1982)Google Scholar
  14. 14.
    Osuna, E., Freund, R., Girosi, R.: Support vector machines: training and applications. A.I. Memo AIM - 1602. MIT A.I. Lab (1996)Google Scholar
  15. 15.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  16. 16.
    Bennett, K.P., Bredensteiner, E.J.: Duality and geometry in SVM classifiers. In: Proceedings of 17th International Conference on Machine Learning, pp. 57–64 (2000)Google Scholar
  17. 17.
    Crisp, D.J., Burges, C.J.C.: A geometric interpretation of nu-svm classifiers. In: Advances in Neural Information Processing Systems, vol. 12 (1999)Google Scholar
  18. 18.
    Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998),
  19. 19.
    Syed, N.A., Liu, H., Sung, K.K.: A study of support vectors on model independent example selection. In: Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jigang Wang
    • 1
  • Predrag Neskovic
    • 1
  • Leon N. Cooper
    • 1
  1. 1.Institute for Brain and Neural Systems, Physics DepartmentBrown UniversityProvidenceUSA

Personalised recommendations