Pattern classification using neural networks

Authors:
R. P. Lippmann
Affiliations:
MIT Lincoln Lab., Lexington, MA
Venue:
IEEE Communications Magazine
Year:
1989

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Abstract

The author extends a previous review and focuses on feed-forward neural-net classifiers for static patterns with continuous-valued inputs. He provides a taxonomy of neural-net classifiers, examining probabilistic, hyperplane, kernel, and exemplar classifiers. He then discusses back-propagation and decision-tree classifiers; matching classifier complexity to training data; GMDH (generalized method of data handling) networks and high-order nets; K nearest-neighbor classifiers; the feature-map classifier; the learning vector quantizer; hypersphere classifiers; and radial-basis function classifiers