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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Vapnik, V.N. |
| Copyright Year | 1990 |
| 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. |
| Sponsorship | IEEE Computational Intelligence Society |
| Starting Page | 988 |
| Ending Page | 999 |
| Page Count | 12 |
| File Size | 281448 |
| File Format | |
| ISSN | 10459227 |
| Volume Number | 10 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-09-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Statistical learning Machine learning Pattern recognition Loss measurement Support vector machines Algorithm design and analysis Multidimensional systems Risk management Probability distribution |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Computer Science Applications Software |
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