Fastest learning in small-world neural networks

被引：91

作者：

Simard, D ^{[1
]}

Nadeau, L ^{[1
]}

Kröger, H ^{[1
]}

机构：

[1] Univ Laval, Dept Phys, St Foy, PQ G1K 7P4, Canada

来源：

PHYSICS LETTERS A | 2005年 / 336卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

10.1016/j.physleta.2004.12.078

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We fmd that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition. (C) 2005 Elsevier B.V. All rights reserved.

引用

页码：8 / 15

页数：8

共 24 条

[1] Internet -: Diameter of the World-Wide Web [J].