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Proceedings Paper

Noise sensitivity of static neural network classifiers
Author(s): Steven D. Beck; Joydeep Ghosh
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Paper Abstract

A variety of artificial neural networks are evaluated for their classification abilities under noisy inputs. These networks include feedforward networks, localized basis function networks, and exemplar classifiers. The performance of radial basis function classifiers deteriorate rapidly in the presence of noise, but elliptical basis variants are able to adapt to extraneous input components quite robustly. For feedforward networks, selective pruning of weights based on an `optimal brain damage' approach helps in noise-tolerant classification. Results from a radar classification problem are presented.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140061
Show Author Affiliations
Steven D. Beck, Tracor, Inc. (United States)
Joydeep Ghosh, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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