Learning algorithms for classification: A comparison on handwritten digit recognition



This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.


Yann LeCun
LD Jackel
Leon Bottou
Corinna Cortes
John S Denker
Harris Drucker
Isabelle Guyon
Urs A Muller
Eduard Sackinger
Isabelle Guyon
Vladimir Vapnik