Statistics and Computing

, Volume 14, Issue 3, pp 199–222

A tutorial on support vector regression

  • Alex J. Smola
  • Bernhard Schölkopf
Article

DOI: 10.1023/B:STCO.0000035301.49549.88

Cite this article as:
Smola, A.J. & Schölkopf, B. Statistics and Computing (2004) 14: 199. doi:10.1023/B:STCO.0000035301.49549.88

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.

machine learningsupport vector machinesregression estimation

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Alex J. Smola
    • 1
  • Bernhard Schölkopf
    • 2
  1. 1.RSISEAustralian National UniversityCanberraAustralia
  2. 2.Max-Planck-Institut für biologische KybernetikTübingenGermany