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Proceedings Paper

Feedforward networks with hierarchical structure and local learning
Author(s): Ernest Robert McCurley; Mark T. Miller
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Paper Abstract

A specialized multilayer perceptron architecture called the adaptive neighborhood network is presented. Geometric and statistical information about pattern distributions replace traditional heuristics for network structure and initialization. As a result, adaptive neighborhood networks train quickly and simply, and perform well in certain classification applications.

Paper Details

Date Published: 1 July 1992
PDF: 8 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140096
Show Author Affiliations
Ernest Robert McCurley, Georgia Institute of Technology (United States)
Mark T. Miller, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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