Non-Bayesian Optical Inference Machines

Ivan Kadar; George Eichmann

doi:10.1117/12.936985

8 January 1987 Non-Bayesian Optical Inference Machines

Ivan Kadar, George Eichmann

Proceedings Volume 0700, 1986 Intl Optical Computing Conf; (1987) https://doi.org/10.1117/12.936985
Event: 1986 International Computing Conference, 1986, Jerusalem, Israel

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.

Citation Download Citation

Ivan Kadar and George Eichmann "Non-Bayesian Optical Inference Machines", Proc. SPIE 0700, 1986 Intl Optical Computing Conf, (8 January 1987); https://doi.org/10.1117/12.936985

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