Classification power of multiple-layer artificial neural networks

Ernest Robert McCurley; Kenyon R. Miller; Ronald Shonkwiler

doi:10.1117/12.21208

1 August 1990 Classification power of multiple-layer artificial neural networks

Ernest Robert McCurley, Kenyon R. Miller, Ronald Shonkwiler

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21208
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

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

Citation Download Citation

Ernest Robert McCurley, Kenyon R. Miller, and Ronald Shonkwiler "Classification power of multiple-layer artificial neural networks", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21208

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