network chip and automatic learning
Handwritten digit recognition: Applications of neural network chips and automatic learning
LeCun, Y. | Jackel, L. | Boser, B. | Denker, J.
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.