A Study of the Effect of Noise Injection on the Training of Artificial Neural Networks

被引：0

作者：

Jiang, Yulei ^{[1
]}

Zur, Richard M. ^{[1
]}

Pesce, Lorenzo L. ^{[1
]}

Drukker, Karen ^{[1
]}

机构：

[1] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA

来源：

IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6 | 2009年

关键词：

COMPUTER-AIDED DIAGNOSIS; MAXIMUM-LIKELIHOOD-ESTIMATION; SCREENING MAMMOGRAPHY; ROC CURVES; PERFORMANCE;

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：

We studied the effect of noise injection in overcoming the problem of overtraining in the training of artificial neural networks (ANNs) in comparison with other common approaches for overcoming this problem such as early stopping of the ANN training process and weight decay (which is similar to Bayesian artificial neural networks). We found from simulation studies and studies of a computer-aided diagnosis application that noise injection is effective in overcoming overtraining and is as effective as, or even more effective than, early stopping and weight decay.

引用

页码：2784 / 2788

页数：5

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