Combining neural networks using the ranking figure of merit

Khaled A. Al-Ghoneim; Bhagavatula Vijaya Kumar

doi:10.1117/12.235912

22 March 1996 Combining neural networks using the ranking figure of merit

Khaled A. Al-Ghoneim, Bhagavatula Vijaya Kumar

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235912
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Khaled A. Al-Ghoneim and Bhagavatula Vijaya Kumar "Combining neural networks using the ranking figure of merit", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235912

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available