Chapter

Knowledge-Based Intelligent Information and Engineering Systems

Volume 2773 of the series Lecture Notes in Computer Science pp 386-392

Multicategory Incremental Proximal Support Vector Classifiers

  • Amund TveitAffiliated withDepartment of Computer and Information Science, Norwegian University of Science and Technology
  • , Magnus Lie HetlandAffiliated withDepartment of Computer and Information Science, Norwegian University of Science and Technology

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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.