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Proceedings Paper

Perceptrons for photon-limited image classification
Author(s): Marek Elbaum; Mark Syrkin
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Paper Abstract

Perceptron learning for the Bayesian classification problem has been analyzed in the framework of Markov diffusion under the influence of competing stochastic forces. The analytic solution for the one-layer architecture yields an immediate relationship between the statistics of the input signal and the weight configuration built by the Perceptron. The computer simulation of the Perceptron learning classification of image-like patterns governed by the Poisson distribution demonstrates a dependence of the learning dynamics on the number of layers and the size of the training data set.

Paper Details

Date Published: 16 September 1992
PDF: 13 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139999
Show Author Affiliations
Marek Elbaum, Electro-Optical Sciences, Inc. (United States)
Mark Syrkin, SUNY/Maritime College (United States)

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

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