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) |
|---|---|
| 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
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Kulkarni, S. R., Lugosi, G., & Venkatesh, S. S. (1998). Learning pattern classification-A survey. IEEE Transactions on Information Theory, 44(6), 2178-2206. https://doi.org/10.1109/18.720536