Handwritten character recognition using the hybrid learning rule
Richard J. Wood, Michael A. Gennert · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.