Avoiding poor local minima in training multilayer perceptrons

被引：0

作者：

Lo, JTH ^{[1
]}

Bassu, D ^{[1
]}

机构：

[1] Univ Maryland Baltimore Cty, Dept Math & Stat, Baltimore, MD 21228 USA

来源：

8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING | 2001年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A training method is reported that adaptively selects a risk-averting error criterion to suit the function under approximation and the noise statistics of the training data so as to include fine features of the function and its segments under-represented in the training data. A companion paper also presented at ICONIP'01 proves that the method has the ability to avoid poor local minima of the selected criterion. Numerical examples given illustrate the efficacy of this training method.

引用

页码：1327 / 1332

页数：6

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