Coding Generic Features For Recognition In A Neural Network
Ganapathy Krishnan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1989
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.