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 (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists. Index Terms-Classification, learning, statistical pattern recognition, survey review.
Original language | English (US) |
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Pages (from-to) | 2178-2206 |
Number of pages | 29 |
Journal | IEEE Transactions on Information Theory |
Volume | 44 |
Issue number | 6 |
DOIs | |
State | Published - Dec 1 1998 |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences