Coding Generic Features For Recognition In A Neural Network

Ganapathy Krishnan

doi:10.1117/12.969334

21 March 1989 Coding Generic Features For Recognition In A Neural Network

Ganapathy Krishnan

Proceedings Volume 1095, Applications of Artificial Intelligence VII; (1989) https://doi.org/10.1117/12.969334
Event: SPIE 1989 Technical Symposium on Aerospace Sensing, 1989, Orlando, FL, United States

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.

Citation Download Citation

Ganapathy Krishnan "Coding Generic Features For Recognition In A Neural Network", Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); https://doi.org/10.1117/12.969334

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