Multicategory Incremental Proximal Support Vector Classifiers

  • Amund Tveit
  • Magnus Lie Hetland
Conference paper

DOI: 10.1007/978-3-540-45224-9_54

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)
Cite this paper as:
Tveit A., Hetland M.L. (2003) Multicategory Incremental Proximal Support Vector Classifiers. In: Palade V., Howlett R.J., Jain L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science, vol 2773. Springer, Berlin, Heidelberg

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.

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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

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