Advertisement

Machine Learning

, Volume 46, Issue 1–3, pp 351–360 | Cite as

Convergence of a Generalized SMO Algorithm for SVM Classifier Design

  • S.S. Keerthi
  • E.G. Gilbert
Article

Abstract

Convergence of a generalized version of the modified SMO algorithms given by Keerthi et al. for SVM classifier design is proved. The convergence results are also extended to modified SMO algorithms for solving ν-SVM classifier problems.

support vector machine SMO algorithm convergence 

References

  1. Chang, C. C., Hsu, C.W., & Lin, C. J. (1999). The analysis of decomposition methods for support vector machines. In Proceedings of the Workshop on Support Vector Machines, Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 99).Google Scholar
  2. Crisp, D. & Burges, C. (1999). A geometric interpretation of v-SVM classifiers. Neural Information Processing Systems Conference, Denver, CO, USA.Google Scholar
  3. Joachims, T. (1998). Marking large-scale support vector machine learning practical. In B. Schölkopf, C. Burges, & A. Smola, Advances in kernel methods: Support vector machines. Cambridge, MA: MIT Press.Google Scholar
  4. Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (1999). Improvements to Platt's SMO algorithm for SVM classifier design. Technical Report CD-99-14, Control Division, Dept. of Mechanical and Production Engineering, National University of Singapore.Google Scholar
  5. Lin, C. J. (2000). On the convergence of the decomposition method for support vector machines. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Sept. 2000.Google Scholar
  6. Platt, J. C. (1998). Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, & A. Smola (Eds.). Advances in kernel methods: Support vector machines, Cambridge, MA: MIT Press.Google Scholar
  7. Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the support of a high-dimensional distribution. Tr 99-87, Microsoft Research.Google Scholar
  8. Schölkopf, B., Smola, A. J., Williamson, R., & Bartlett, P. (1998). New support vector algorithms. Neuro COLT Technical Report TR-1998-031, Royal Holloway College.Google Scholar
  9. Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K. (1999). Improvements to the SMO algorithm for SVM regression. Technical Report CD-99-16, Control Division, Dept. of Mechanical and Production Engineering, National University of Singapore.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • S.S. Keerthi
    • 1
  • E.G. Gilbert
    • 2
  1. 1.Department of Mechanical EngineeringNational University of SingaporeSingapore
  2. 2.Department of Aerospace EngineeringUniversity of MichiganAnn ArborUSA

Personalised recommendations