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We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and speciically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1% error rate and about a 9% reject(More)
M odels of neural networks are receiving widespread attention as potential new architectures for computing systems. The models we consider here consist of highly interconnected networks of simple computing elements. A computation is performed collectively by the whole network with the activity distributed over all the computing elements. This collective(More)
This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low(More)
MOS charge storage has been demonstrated as an effective method to store the weights in VLSI implementations of neural network models by several workers 2. However, to achieve the full power of a VLSI implementation of an adaptive algorithm, the learning operation must built into the circuit. We have fabricated and tested a circuit ideal for this purpose by(More)
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