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R. E. Howard
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Publisher: Journals Gateway
Neural Computation (1989) 1 (4): 541–551.
Published: 01 December 1989
Abstract
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The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.