Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published July 1994 | Published
Book Section - Chapter Open

A statistical analysis of neural computation


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.

Additional Information

© 1994 IEEE.

Attached Files

Published - 00394753.pdf


Files (75.5 kB)
Name Size Download all
75.5 kB Preview Download

Additional details

August 20, 2023
August 20, 2023