International Journal of Computer Vision

, Volume 38, Issue 1, pp 9–13

Statistical Learning Theory: A Primer

  • Theodoros Evgeniou
  • Massimiliano Pontil
  • Tomaso Poggio
Article

DOI: 10.1023/A:1008110632619

Cite this article as:
Evgeniou, T., Pontil, M. & Poggio, T. International Journal of Computer Vision (2000) 38: 9. doi:10.1023/A:1008110632619

Abstract

In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well as emerging learning techniques such as Regularization Networks and Support Vector Machines which can be justified in term of the same induction principle.

VC-dimension structural risk minimization regularization networks support vector machines 

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Theodoros Evgeniou
    • 1
  • Massimiliano Pontil
    • 1
  • Tomaso Poggio
    • 1
  1. 1.Center for Biological and Computational Learning, Artificial Intelligence LaboratoryMITCambridgeUSA

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