Automatic early stopping using cross validation: quantifying the criteria

@article{Prechelt1998AutomaticES,
  title={Automatic early stopping using cross validation: quantifying the criteria},
  author={Lutz Prechelt},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={1998},
  volume={11 4},
  pages={761-767}
}
Cross validation can be used to detect when overfitting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overfitting ('early stopping'). The exact criterion used for cross validation based early stopping, however, is chosen in an ad-hoc fashion by most researchers or training is stopped interactively. To aid a more well-founded selection of the stopping criterion, 14 different automatic stopping criteria from three classes were… CONTINUE READING

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