Pattern classification using neural networks

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.< >