Comparing different neural network architectures for classifying handwritten digits

I. Guyon, I. Poujaud, L. Personnaz, G. Dreyfus, J. Denker, Yann LeCun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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)
Title of host publicationIJCNN Int Jt Conf Neural Network
Editors Anon
PublisherPubl by IEEE
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

Fingerprint

Ad hoc networks
Network architecture
Classifiers
Polynomials
Neural networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Guyon, I., Poujaud, I., Personnaz, L., Dreyfus, G., Denker, J., & LeCun, Y. (1989). Comparing different neural network architectures for classifying handwritten digits. In Anon (Ed.), IJCNN Int Jt Conf Neural Network (pp. 127-132). Publ by IEEE.

Comparing different neural network architectures for classifying handwritten digits. / Guyon, I.; Poujaud, I.; Personnaz, L.; Dreyfus, G.; Denker, J.; LeCun, Yann.

IJCNN Int Jt Conf Neural Network. ed. / Anon. Publ by IEEE, 1989. p. 127-132.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Guyon, I, Poujaud, I, Personnaz, L, Dreyfus, G, Denker, J & LeCun, Y 1989, Comparing different neural network architectures for classifying handwritten digits. in Anon (ed.), IJCNN Int Jt Conf Neural Network. Publ by IEEE, pp. 127-132, IJCNN International Joint Conference on Neural Networks, Washington, DC, USA, 6/18/89.
Guyon I, Poujaud I, Personnaz L, Dreyfus G, Denker J, LeCun Y. Comparing different neural network architectures for classifying handwritten digits. In Anon, editor, IJCNN Int Jt Conf Neural Network. Publ by IEEE. 1989. p. 127-132
Guyon, I. ; Poujaud, I. ; Personnaz, L. ; Dreyfus, G. ; Denker, J. ; LeCun, Yann. / Comparing different neural network architectures for classifying handwritten digits. IJCNN Int Jt Conf Neural Network. editor / Anon. Publ by IEEE, 1989. pp. 127-132
@inproceedings{5c33258e3d1b402bbeb0df6641072ea9,
title = "Comparing different neural network architectures for classifying handwritten digits",
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.",
author = "I. Guyon and I. Poujaud and L. Personnaz and G. Dreyfus and J. Denker and Yann LeCun",
year = "1989",
language = "English (US)",
pages = "127--132",
editor = "Anon",
booktitle = "IJCNN Int Jt Conf Neural Network",
publisher = "Publ by IEEE",

}

TY - GEN

T1 - Comparing different neural network architectures for classifying handwritten digits

AU - Guyon, I.

AU - Poujaud, I.

AU - Personnaz, L.

AU - Dreyfus, G.

AU - Denker, J.

AU - LeCun, Yann

PY - 1989

Y1 - 1989

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0024944137&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024944137&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0024944137

SP - 127

EP - 132

BT - IJCNN Int Jt Conf Neural Network

A2 - Anon, null

PB - Publ by IEEE

ER -