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Proceedings Paper

Comparison of Mahalanobis distance, polynomial, and neural net classifiers
Author(s): James H. Hughen; Kenneth Rex Hollon; David C. Lai
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Paper Abstract

In this study we consider a family of polynomial classifiers and compare the performance of these classifiers to the Mahalanobis Distance classifier and to two types of artificial neural networks- -multilayer perceptrons and high-order neural networks. The well-known Mahalanobis Distance classifier is based on the assumption that the underlying probability distributions are Gaussian. The neural network classifiers and polynomial classifiers make no assumptions regarding underlying distributions. The decision boundaries of the polynomial classifier can be made to be arbitrarily nonlinear corresponding to the degree of the polynomial hence comparable to those of the neural networks. Further we describe both iterative gradient descent and batch procedures by which the polynomial classifiers can be trained. These procedures provide much faster training than that achievable for multilayer perceptrons trained via backpropagation. We demonstrate that the polynomial classifier and high-order neural network can be equated thereby implying that the classification power of the multilayer perceptron can be achieved while retaining the ease of training advantages of the polynomial classifiers. 1.

Paper Details

Date Published: 1 August 1990
PDF: 11 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21206
Show Author Affiliations
James H. Hughen, Martin Marietta Corp. (United States)
Kenneth Rex Hollon, Martin Marietta Corp. (United States)
David C. Lai, Martin Marietta Corp. (United States)

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

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