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Backpropagation Applied to Handwritten Zip Code Recognition

Backpropagation Applied to Handwritten Zip Code Recognition 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computation MIT Press

Backpropagation Applied to Handwritten Zip Code Recognition

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References (13)

Publisher
MIT Press
Copyright
© 1989 Massachusetts Institute of Technology
ISSN
0899-7667
eISSN
1530-888X
DOI
10.1162/neco.1989.1.4.541
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Neural ComputationMIT Press

Published: Dec 1, 1989

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