Efficient Sequential Minimal Optimisation of Support Vector Classifiers

  • Gavin C. Cawley


This paper describes a simple modification to the sequential minimal optimisation (SMO) training algorithm for support vector machine (SVM) classifiers, reducing training time at the expense of a small increase in memory used proportional to the number of training patterns. Results obtained on real-world pattern recognition tasks indicate that the proposed modification can more than halve the average training time.


Support Vector Machine Support Vector Lagrange Multiplier Training Time Training Pattern 
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. [1]
    B. Boser, I. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,’ in Proceedings of the fifth annual workshop on computational learning theory, (Pittsburgh), pp. 144–152, ACM, 1992.Google Scholar
  2. [2]
    C. Cortes and V. Vapnik, “Support vector networks,” Machine Learning, vol. 20, pp. 1–25, 1995.Google Scholar
  3. [3]
    V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.CrossRefMATHGoogle Scholar
  4. [4]
    V. N. Vapnik, Statistical Learning Theory. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control, New York: Wiley, 1998.Google Scholar
  5. [5]
    N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge, U.K.: Cambridge University Press, 2000.CrossRefGoogle Scholar
  6. [6]
    J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods — Support Vector Learning (B. Schölkopf, C. J. C. Burges, and A. J. Smola, eds.), ch. 12, pp. 185–208, Cambridge, Massachusetts: MIT Press, 1999.Google Scholar
  7. [7]
    E. Osuna, R. Freund, and F. Girosi, “An improved training algorithm for support vector machines,” in Neural Networks for Signal Processing VII — Proceedings of the 1997 IEEE Workshop (J. Principe, L. Gile, N. Morgan, and E. Wilson, eds.), (New York), pp. 276–285, IEEE, 1997.Google Scholar
  8. [8]
    K. Arnold, J. Gosling, and D. Holmes, The Java Programming Language. Addison-Wesley, third ed., 2000.Google Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Gavin C. Cawley
    • 1
  1. 1.School of Information SystemsUniversity of East AngliaNorwich, NorfolkUK

Personalised recommendations