This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Sep 1999
Volume: 10,
Issue: 5
On page(s): 988-999
ISSN: 1045-9227
References Cited: 30
CODEN: ITNNEP
INSPEC Accession Number: 6362641
DOI: 10.1109/72.788640
Posted online: 2002-08-06 22:37:20.0
Abstract
Statistical learning theory was introduced in the late 1960's.
Until the 1990's it was a purely theoretical analysis of the problem of
function estimation from a given collection of data. In the middle of
the 1990's new types of learning algorithms (called support vector
machines) based on the developed theory were proposed. This made
statistical learning theory not only a tool for the theoretical analysis
but also a tool for creating practical algorithms for estimating
multidimensional functions. This article presents a very general
overview of statistical learning theory including both theoretical and
algorithmic aspects of the theory. The goal of this overview is to
demonstrate how the abstract learning theory established conditions for
generalization which are more general than those discussed in classical
statistical paradigms and how the understanding of these conditions
inspired new algorithmic approaches to function estimation
problems
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