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- W2056535444 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." @default.
- W2056535444 created "2016-06-24" @default.
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- W2056535444 date "1989-03-21" @default.
- W2056535444 modified "2023-09-23" @default.
- W2056535444 title "Coding Generic Features For Recognition In A Neural Network" @default.
- W2056535444 doi "https://doi.org/10.1117/12.969334" @default.
- W2056535444 hasPublicationYear "1989" @default.
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