Regularization Networks and Support Vector Machines

  • Theodoros Evgeniou
  • Massimiliano Pontil
  • Tomaso Poggio
Article

DOI: 10.1023/A:1018946025316

Cite this article as:
Evgeniou, T., Pontil, M. & Poggio, T. Advances in Computational Mathematics (2000) 13: 1. doi:10.1023/A:1018946025316

Abstract

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case.

regularization Radial Basis Functions Support Vector Machines Reproducing Kernel Hilbert Space Structural Risk Minimization 

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 and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA E-mail:

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