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Proceedings Paper

Combining neural networks using the ranking figure of merit
Author(s): Khaled A. Al-Ghoneim; Bhagavatula Vijaya Kumar
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Paper Abstract

The neural network community has recently shown a considerably interest in combining multiple neural networks (NNs). Such a combination usually improves the performance over a single NN because different NNs can complement each other. To achieve improved performance, the individual NNs must be trained independently. In this paper, three NNs are trained using the Ranking Figure of Merit objective function (with different parameters) that we introduced last year. We introduce a new method of combining NNs, which we call pooled objective function. The objective function is calculated for each NN and averaged to arrive at the pooled objective function. The combined vote is the class with the best pooled objective function. It is shown that the frequently used scheme of averaging the outputs is equivalent to the pooled mean squared error.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235912
Show Author Affiliations
Khaled A. Al-Ghoneim, Carnegie Mellon Univ. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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