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Proceedings Paper

Coding Generic Features For Recognition In A Neural Network
Author(s): Ganapathy Krishnan
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Paper Abstract

This paper describes an experiment in recognizing simple hand-drawn shapes on the basis of generic features which are psychologically motivated. A coarse coding scheme is used to represent the input features. The input features are mapped to the appropriate output category in a single-layer neural network using three different learning rules: the Hebbian rule, the Delta rule, and a modification of the Hebbian rule. The shape recognition algorithm was tested in three different domains with results comparable to conventional recognition techniques. The advantage of the scheme proposed here is its generality, and its ability to learn from examples.

Paper Details

Date Published: 21 March 1989
PDF: 8 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969334
Show Author Affiliations
Ganapathy Krishnan, Stetson University (United States)

Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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