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Proceedings Paper

Classification power of multiple-layer artificial neural networks
Author(s): Ernest Robert McCurley; Kenyon R. Miller; Ronald Shonkwiler
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Paper Abstract

Feedforward networks are artifical neural networks composed of successive layers of neurons. In this paper we use mathematical and geometric analysis to investigate properties of classifiers feedforward networks composed of neurons having threshold activation functions. The focus of this investigation is the relationship between the classification power of these networks and the number of layers composing them. We show that regions classifiable by simple twolayer classifiers also known as perceptrons are closed under region complementation and a limited form of region intersection. The proof of these results leads to a method for constructing twolayer classifiers for complicated regions. 1

Paper Details

Date Published: 1 August 1990
PDF: 11 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21208
Show Author Affiliations
Ernest Robert McCurley, Georgia Institute of Technology (United States)
Kenyon R. Miller, Georgia Institute of Technology (United States)
Ronald Shonkwiler, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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