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Proceedings Paper

Evolving neural network pattern classifiers
Author(s): John R. McDonnell; Donald E. Waagen; Ward C. Page
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Paper Abstract

This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162036
Show Author Affiliations
John R. McDonnell, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Donald E. Waagen, Naval Command, Control and Ocean Surveillance Ctr. (United States)
Ward C. Page, Naval Command, Control and Ocean Surveillance Ctr. (United States)


Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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