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Model selection in neural networks
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DOI：10.1016/S0893-6080(98)00117-8.png)
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摘要
En 中文
In this article, we examine how model selection in neural networks can be guided by statistical procedures such as hypothesis tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification problems encountered. We then propose five specification strategies based on different statistical procedures and compare them in a simulation study. As the results of the study are promising, it is suggested that a statistical analysis should become an integral part of neural network modeling. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
neural networks
statistical inference
model selection
identification
information criteria
cross validation
pruning
Monte Carlo simulation
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6.3
论文数:
8.2K
被引数:
3.0W
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