A Comparison of Some Error Estimates for Neural Network Models 论文
1996Neural Computation引用 294
Neural Networks and ApplicationsControl Systems and IdentificationFault Detection and Control Systems
详细信息
- 发表期刊/会议
- Neural Computation
- 发表日期
- 1996-01-01
- 发表年份
- 1996
关键词
Neural Networks and ApplicationsControl Systems and IdentificationFault Detection and Control Systems
摘要
We discuss a number of methods for estimating the standard error of predicted values from a multilayer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the “sandwich” estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights.