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Support Vector Machines in R

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  • Karatzoglou, Alexandros
  • Meyer, David
  • Hornik, Kurt

Abstract

Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.

Suggested Citation

  • Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    • Handle: RePEc:jss:jstsof:v:015:i09
    DOI: http://hdl.handle.net/10.18637/jss.v015.i09
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    1. Karatzoglou, Alexandros & Smola, Alexandros & Hornik, Kurt & Zeileis, Achim, 2004. "kernlab - An S4 Package for Kernel Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i09).
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    19. Oosterlinck, Dieter & Benoit, Dries F. & Baecke, Philippe, 2020. "From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1011-1024.
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