Relating Bayesian learning to training in recurrent networks

被引：0

作者：

Spiegel, R ^{[1
]}

机构：

[1] Univ London Goldsmiths Coll, Dept Comp, London SE14 6NW, England

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003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：

It will be demonstrated that a recurrent neural network relying on an error correcting (truncated gradient descent) learning algorithm and a localist coding scheme is able to converge to,a solution that would be expected from Bayesian learning. This is possible even without implementing Bayes theorem and without assigning prior probabilities to the model.

引用

页码：908 / 913

页数：6

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