Share Email Print

Proceedings Paper

Increasing classification accuracy using multiple-neural-network schemes
Author(s): George N. Bebis; Michael Georgiopoulos; George M. Papadourakis; Gregory L. Heileman
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Back propagation neural networks have been widely used as classifiers in many complex classification tasks. However, early experimental results show that as the number of classes involved in a classification task increases, the classification accuracy of these networks decreases, especially in the presence of noisy inputs. In addition, larger size networks are needed to be utilized in such cases, a fact that may not always be possible. In order to overcome both of these undesirable effects a new approach is proposed in this paper which utilizes multiple, relatively small size networks to perform the classification task. This approach has been applied on a machine printed character recognition experiment and it has demonstrated better classification accuracy than the one exhibited by the single, larger size, network approach.

Paper Details

Date Published: 16 September 1992
PDF: 11 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140001
Show Author Affiliations
George N. Bebis, Univ. of Central Florida (United States)
Michael Georgiopoulos, Univ. of Central Florida (United States)
George M. Papadourakis, Institute of Computer Science (Greece)
Gregory L. Heileman, Univ. of New Mexico (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?