Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients

  • Amund Tveit
  • Magnus Lie Hetland
  • Håavard Engum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)


This paper presents an efficient approach for supporting decremental learning for incremental proximal support vector machines (SVM). The presented decremental algorithm based on decay coefficients is compared with an existing window-based decremental algorithm, and is shown to perform at a similar level in accuracy, but providing significantly better computational performance.


Support Vector Machine Concept Drift Support Vector Machine Method Operational Research Society Increment Size 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

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

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