A comparison of neural network and nearest-neighbor classifiers of handwritten lower-case letters
Thomas M. English, Maria del Pilar Gomez-Gil, William J. B. Oldham · 2002
The authors apply k-nearest-neighbor classifiers, fully-connected networks, and networks of an architecture devised by LeCun to the problem of recognizing handwritten (cursive) lower-case letters. Results reported differ from those of studies involving hand-printed characters. LeCun networks give higher accuracy (77%) than fully-connected networks (74%), which in turn give higher accuracy than k-nearest neighbor classifiers (71%). It is observed that training with an error criterion based on the L/sup 10/ norm allows LeCun networks to avoid some local minima encountered when the squared error (L/sup 2/) criterion is used.>