Overtraining , Regularization , and Searching for Minimum Inneural Networksj

@inproceedings{Oberg1992OvertrainingR,
  title={Overtraining , Regularization , and Searching for Minimum Inneural Networksj},
  author={Oberg and Lennart Ljung},
  year={1992}
}
Neural network models for dynamical systems have been subject of considerable interest lately. They are often characterized by the fact that they use a fairly large amount of parameters. Here we address the problem why this can be done without the usual penalty in terms of a large variance error. We show that regularization is a key explanation, and that terminating a gradient search (\backpropagation") before the true criterion minimum is found is a way of achieving regularization. This, among… CONTINUE READING
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