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Proceedings Paper

Handwritten character recognition using the hybrid learning rule
Author(s): Richard J. Wood; Michael A. Gennert
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Paper Abstract

The hybrid learning rule is a novel learning rule that combines the Hebbian learning rule and the back propagation algorithm. This novel learning rule was applied to the problem of isolated handwritten character recognition. The problem domain was limited to ten letters, which may be rotated or translated. The performance of the hybrid learning rule on this problem domain was measured and compared to the performance of the back propagation algorithm. While the hybrid learning rule failed to outperform the back propagation algorithm, it does generate receptive fields similar to those found by other researchers.

Paper Details

Date Published: 16 December 1992
PDF: 7 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130847
Show Author Affiliations
Richard J. Wood, Worcester Polytechnic Institute (United States)
Michael A. Gennert, Worcester Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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