An overview of statistical learning theory
- 1 September 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 10 (5) , 988-999
- https://doi.org/10.1109/72.788640
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.Keywords
This publication has 17 references indexed in Scilit:
- Structural risk minimization over data-dependent hierarchiesIEEE Transactions on Information Theory, 1998
- An Equivalence Between Sparse Approximation and Support Vector MachinesNeural Computation, 1998
- Nonlinear Component Analysis as a Kernel Eigenvalue ProblemNeural Computation, 1998
- The connection between regularization operators and support vector kernelsNeural Networks, 1998
- The Glivenko-Cantelli problem, ten years laterJournal of Theoretical Probability, 1996
- Regularization Theory and Neural Networks ArchitecturesNeural Computation, 1995
- A training algorithm for optimal margin classifiersPublished by Association for Computing Machinery (ACM) ,1992
- Spline Models for Observational DataPublished by Society for Industrial & Applied Mathematics (SIAM) ,1990
- Learnability and the Vapnik-Chervonenkis dimensionJournal of the ACM, 1989
- Learning Process in an Asymmetric Threshold NetworkPublished by Springer Nature ,1986