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The Nature of Statistical Learning Theory (1999)
| Citations: | 13209 - 32 self |
BibTeX
@MISC{Vapnik99thenature,
author = {Vladimir N. Vapnik},
title = {The Nature of Statistical Learning Theory},
year = {1999}
}
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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. A more
Keyphrases
statistical learning theory theoretical analysis support vector machine new type new algorithmic approach general overview function estimation classical statistical paradigm abstract statistical practical algorithm developed theory abstract learning theory algorithmic aspect multidimensional function estimation problem
