Globally optimal parameters for on-line learning in multilayer neural networks

被引：31

作者：

Saad, D

Rattray, M

机构：

[1] Department of Computer Science and Applied Mathematics, Aston University, Birmingham

来源：

PHYSICAL REVIEW LETTERS | 1997年 / 79卷 / 13期

关键词：

D O I：

10.1103/PhysRevLett.79.2578

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.

引用

页码：2578 / 2581

页数：4

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