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Proceedings Paper

Non-Bayesian Optical Inference Machines
Author(s): Ivan Kadar; George Eichmann
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Paper Abstract

In a recent paper, Eichmann and Caulfield) presented a preliminary exposition of optical learning machines suited for use in expert systems. In this paper, we extend the previous ideas by introducing learning as a means of reinforcement by information gathering and reasoning with uncertainty in a non-Bayesian framework2. More specifically, the non-Bayesian approach allows the representation of total ignorance (not knowing) as opposed to assuming equally likely prior distributions.

Paper Details

Date Published: 8 January 1987
PDF: 3 pages
Proc. SPIE 0700, 1986 Intl Optical Computing Conf, (8 January 1987); doi: 10.1117/12.936985
Show Author Affiliations
Ivan Kadar, Grumman Corporation (United States)
George Eichmann, City College of the City University of New York (United States)

Published in SPIE Proceedings Vol. 0700:
1986 Intl Optical Computing Conf
Asher A. Friesem; Emanuel Marom; Joseph Shamir, Editor(s)

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