Backpropagation Applied to Handwritten Zip Code Recognition

被引：8142

作者：

LeCun, Y. ^{[1
]}

Boser, B. ^{[1
]}

Denker, J. S. ^{[1
]}

Henderson, D. ^{[1
]}

Howard, R. E. ^{[1
]}

Hubbard, W. ^{[1
]}

Jackel, L. D. ^{[1
]}

机构：

[1] AT&T Bell Labs, Holmdel, NJ 07733 USA

来源：

NEURAL COMPUTATION | 1989年 / 1卷 / 04期

关键词：

D O I：

10.1162/neco.1989.1.4.541

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.

引用

页码：541 / 551

页数：11

共 15 条

[1] What Size Net Gives Valid Generalization? [J].