Comparing different neural network architectures for classifying handwritten digits

I. Guyon, I. Poujaud, L. Personnaz, G. Dreyfus, J. Denker, Y. Le Cun

Research output: Contribution to conferencePaper

Abstract

An evaluation is made of several neural network classifiers, comparing their performance on a typical problem, namely handwritten digit recognition. For this purpose, the authors use a database of handwritten digits, with relatively uniform handwriting styles. The authors propose a novel of way of organizing the network architectures by training several small networks so as to deal separately with subsets of the problem, and then combining the results. This approach works in conjunction with various techniques including: layered networks with one or several layers of adaptive connections, fully connected recursive networks, ad hoc networks with no adaptive connections, and architectures with second-degree polynomial decision surfaces.

Original languageEnglish (US)
Pages127-132
Number of pages6
StatePublished - 1989
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

ASJC Scopus subject areas

  • Engineering(all)

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