A New Model Selection Method for SVM

  • G. Lebrun
  • O. Lezoray
  • C. Charrier
  • H. Cardot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


In this paper, a new learning method is proposed to build Support Vector Machines (SVMs) Binary Decision Functions (BDF) of reduced complexity and efficient generalization. The aim is to build a fast and efficient SVM classifier. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blendes recognition rate and complexity of a BDF. Vector Quantization (VQ) is used to simplify the training set. A model selection based on the selection of the simplification level, of a feature subset and of SVM hyperparameters is performed to optimize the DFQ. Search space for selecting the best model being huge, Tabu Search (TS) is used to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.


Support Vector Machine Feature Selection Recognition Rate Tabu Search Feature Subset 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Platt, J.: Fast training of SVMs using sequential minimal optimization, Advances in kernel methods-support vector learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  2. Yu, H., Yang, J., Han, J.: Classifying large data sets using SVM with hierarchical clusters. In: SIGKDD, pp. 306–315 (2003)Google Scholar
  3. Lebrun, G., Charrier, C., Cardot, H.: SVM training time reduction using vector quantization. In: ICPR, vol. 1, pp. 160–163 (2004)Google Scholar
  4. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. Sofware Available at (2001),
  5. Ou, Y.Y., Chen, C.Y., Hwang, S.C., Oyang, Y.J.: Expediting model selection for SVMs based on data reduction. In: IEEE Proc. SMC, pp. 786–791 (2003)Google Scholar
  6. Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: Fast SVM training on very large data sets. In: JMLR, vol. 6, pp. 363–392 (2005)Google Scholar
  7. Lebrun, G., Charrier, C., Lezoray, O., Meurie, C., Cardot, H.: Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space. In: CAIP, pp. 685–692 (2005)Google Scholar
  8. Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46, 131–159 (2002)MATHCrossRefGoogle Scholar
  9. Chapelle, O., Vapnik, V.: Model selection for support vector machines. Advances in Neural Information Processing Systems 12, 230–236 (1999)Google Scholar
  10. Fröhlich, H., Chapelle, O., Schölkopf, B.: Feature selection for support vector machines using genetic algorithms. IJAIT 13, 791–800 (2004)Google Scholar
  11. Rifkin, R., Klautau, A.: In defense of one-vs-all classification. JMLR 5, 101–141 (2004)MathSciNetGoogle Scholar
  12. Christianini, N.: Dimension reduction in text classification with support vector machines. In: JMLR, vol. 6, pp. 37–53 (2005)Google Scholar
  13. Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic, Dordrecht (1991)Google Scholar
  14. Staelin, C.: Parameter selection for support vector machines (2002),
  15. Glover, F., Laguna, M.: Tabu search. Kluwer Academic Publishers, Dordrecht (1997)MATHGoogle Scholar
  16. Korycinski, D., Crawford, M.M., Barnes, J.W.: Adaptive feature selection for hyperspectral data analysis. In: SPIE, vol. 5238, pp. 213–225 (2004)Google Scholar
  17. Vapnik, V.N.: Statistical Learning Theory. Wiley edn., New York (1998)MATHGoogle Scholar
  18. Blake, C., Merz, C.: Uci repository of machine learning databases. In: Advances in kernel methods, support vector learning (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • G. Lebrun
    • 1
  • O. Lezoray
    • 1
  • C. Charrier
    • 1
  • H. Cardot
    • 2
  1. 1.IUT Dépt. SRCLUSAC EA 2607, groupe Vision et Analyse d’ImageSaint-LôFrance
  2. 2.Laboratoire Informatique (EA 2101)Université François-Rabelais de ToursToursFrance

Personalised recommendations