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Statistical learning theory: A primer

Evgeniou, T; Pontil, M; Poggio, T; (2000) Statistical learning theory: A primer. INT J COMPUT VISION , 38 (1) 9 - 13.

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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.

Type: Article
Title: Statistical learning theory: A primer
Keywords: VC-dimension, structural risk minimization, regularization networks, support vector machines
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/163506
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