A tutorial on support vector regression

  • Authors:
  • Alex J. Smola;Bernhard Schölkopf

  • Affiliations:
  • RSISE, Australian National University, Canberra 0200, Australia. Alex.Smola@anu.edu.au;Max-Planck-Institut für biologische Kybernetik, 72076 Tübingen, Germany. Bernhard.Schoelkopf@tuebingen.mpg.de

  • Venue:
  • Statistics and Computing
  • Year:
  • 2004

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Abstract

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.