Multicategory Incremental Proximal Support Vector Classifiers

  • Amund Tveit
  • Magnus Lie Hetland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)

Abstract

Support Vector Machines (SVMs) are an efficient data mining approach for classification, clustering and time series analysis. In recent years, a tremendous growth in the amount of data gathered has changed the focus of SVM classifier algorithms from providing accurate results to enabling incremental (and decremental) learning with new data (or unlearning old data) without the need for computationally costly retraining with the old data. In this paper we propose an efficient algorithm for multicategory classification with the incremental proximal SVM introduced by Fung and Mangasarian.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burbidge, R., Buxton, B.F.: An introduction to support vector machines for data mining. In: Sheppee, M. (ed.) Keynote Papers, Young OR12, University of Nottingham, Operational Research Society, pp. 3–15 (2001)Google Scholar
  2. 2.
    Huang, J., Shao, X., Wechsler, H.: Face pose discrimination using support vector machines (svm). In: Proceedings of 14th Int’l Conf. on Pattern Recognition (ICPR 1998), pp. 154–156. IEEE, Los Alamitos (1998)Google Scholar
  3. 3.
    Muller, K.R., Smola, A.J., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: ICANN, pp. 999–1004 (1997)Google Scholar
  4. 4.
    Fung, G., Mangasarian, O.L.: Incremental support vector machine classification. In: Grossman, R., Mannila, H., Motwani, R. (eds.) Proceedings of the Second SIAM International Conference on Data Mining, SIAM, pp. 247–260 (2002)Google Scholar
  5. 5.
    Fung, G., Mangasarian, O.L.: Multicategory Proximal Support Vector Classifiers. Submitted to Machine Learning Journal (2001)Google Scholar
  6. 6.
    Schwefel, H.P., Wegener, I., Weinert, K. (eds.): Natural Computing. Advances in Computational Intelligence: Theory and Practice. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Hettich, S., Bay, S.D.: The UCI KDD archive (1999), http://kdd.ics.uci.edu

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Amund Tveit
    • 1
  • Magnus Lie Hetland
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations