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 validation-based early stopping, however, is usually chosen in an ad-hoc fashion or training is stopped interactively. This trick describes how to select a stopping criterion in a systematic fashion; it is a trick for either speeding learning procedures or improving generalization, whichever is more important in the particular situation. An empirical investigation on multi-layer perceptrons shows that there exists a tradeoff between training time and generalization: From the given mix of 1296 training runs using difierent 12 problems and 24 difierent network architectures I conclude slower stopping criteria allow for small improvements in generalization (here: about 4% on average), but cost much more training time (here: about factor 4 longer on average).
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- Early Stopping - But When?
- Springer Berlin Heidelberg