Share Email Print

Proceedings Paper

Improved probabilistic neural network and its performance relative to other models
Author(s): Joseph Bibb Cain
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents a new extension of the probabilistic neural network which utilizes one additional training pass to obtain significantly improved performance relative to the conventional probabilistic neural network. In addition it automatically sets certain algorithm parameters. The method substantially outperforms K-nearest neighbor techniques for the same number ofnodes. and it also offers performance competitive with LVQ2 which requires much longer training periods. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21187
Show Author Affiliations
Joseph Bibb Cain, Harris Corp. (United States)

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

© SPIE. Terms of Use
Back to Top