Learning pattern classification-a survey
- 1 October 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 44 (6) , 2178-2206
- https://doi.org/10.1109/18.720536
Abstract
Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural networks. The presentation and the large (though nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.Keywords
This publication has 101 references indexed in Scilit:
- Support-vector networksMachine Learning, 1995
- Lower bounds in pattern recognition and learningPattern Recognition, 1995
- Automated design of linear tree classifiersPattern Recognition, 1990
- Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappingsNeural Networks, 1990
- Tree-Structured Classification via Generalized Discriminant AnalysisJournal of the American Statistical Association, 1988
- Tree classifier design with a permutation statisticPattern Recognition, 1986
- A method for the design of binary tree classifiersPattern Recognition, 1983
- Additive estimators for probabilities of correct classificationPattern Recognition, 1978
- A Comparison of Some Multivariate Discrimination ProceduresJournal of the American Statistical Association, 1972
- Problems in the Analysis of Survey Data, and a ProposalJournal of the American Statistical Association, 1963