Open Access
Comparison of learning algorithms for handwritten digit recognition
Yann LeCun,Lawrence D. Jackel,Léon Bottou,Léon Bottou,A. Brunot,Corinna Cortes,Corinna Cortes,John S. Denker,John S. Denker,Harris Drucker,Harris Drucker,Isabelle Guyon,Urs A. Muller,E. Sackinger,Patrice Y. Simard,Patrice Y. Simard,Vladimir Vapnik +16 more
- pp 53-60
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TLDR
This comparison of several learning algorithms for handwritten digits considers not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.Abstract:
COMPARISON OF LEARNINGALGORITHMS FOR HANDWRITTEN DIGITRECOGNITIONY. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes,J. Denker, H. Drucker, I. Guyon, U. M uller,E. Sackinger, P. Simard, and V. VapnikBell Lab oratories, Holmdel, NJ 07733, USAEmail: yann@research.att.comAbstractThis pap er compares the p erformance of several classi er algorithmson a standard database of handwritten digits. We consider not only rawaccuracy, but also rejection, training time, recognition time, and memoryrequirements.1read more
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Handwritten Digit Recognition with a Back-Propagation Network
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TL;DR: Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task, and has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
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