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A statistical analysis of neural computation

Cortese, John A. and Goodman, Rodney M. (1994) A statistical analysis of neural computation. In: Proceedings of 1994 IEEE International Symposium on Information Theory. IEEE , Piscataway, NJ, p. 215. ISBN 0780320158. https://resolver.caltech.edu/CaltechAUTHORS:20190315-142359458

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Abstract

This paper presents an architecture and learning algorithm for a feedforward neural network implementing a two pattern (image) classifier. By considering the input pixels to be random variables, a statistical binary hypothesis (likelihood ratio) test is implemented. A linear threshold separates p[X|H_0] and p[X|H_1], minimizing a risk function. In this manner, a single neuron is considered as a BSC with the pdf error tails probability ε. A Single layer of neurons is viewed as a parallel bank of independent BSC’s, which is equivalent to a single effective BSC representing that layer’s hypothesis testing performance. A multiple layer network is viewed as a cascade of BSC channels, and which again collapses into a single effective BSC.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/isit.1994.394753DOIArticle
Additional Information:© 1994 IEEE.
Record Number:CaltechAUTHORS:20190315-142359458
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190315-142359458
Official Citation:J. A. Cortese and R. M. Goodman, "A statistical analysis of neural computation," Proceedings of 1994 IEEE International Symposium on Information Theory, Trondheim, Norway, 1994, pp. 215-. doi: 10.1109/ISIT.1994.394753
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93886
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Mar 2019 22:16
Last Modified:03 Oct 2019 20:58

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