Share Email Print
cover

Proceedings Paper

MLP iterative construction algorithm
Author(s): Thomas F. Rathbun; Steven K. Rogers; Martin P. DeSimio; Mark E. Oxley
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The MLP Iterative Construction Algorithm (MICA) designs a Multi-Layer Perceptron (MLP) neural network as it trains. MICA adds Hidden Layer Nodes one at a time, separating classes on a pair-wise basis, until the data is projected into a linear separable space by class. Then MICA trains the Output Layer Nodes, which results in an MLP that achieves 100% accuracy on the training data. MICA, like Backprop, produces an MLP that is a minimum mean squared error approximation of the Bayes optimal discriminant function. Moreover, MICA's training technique yields novel feature selection technique and hidden node pruning technique

Paper Details

Date Published: 4 April 1997
PDF: 9 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271467
Show Author Affiliations
Thomas F. Rathbun, Air Force Institute of Technology (United States)
Steven K. Rogers, Battelle Memorial Institute (United States)
Martin P. DeSimio, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

© SPIE. Terms of Use
Back to Top