Automatic early stopping using cross validation: quantifying the criteria
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منابع مشابه
Automatic early stopping using cross validation: quantifying the criteria
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 sele...
متن کاملAppeared in Neural Networks 1998 Automatic Early Stopping Using Cross Validation: Quantifying the Criteria
Cross validation can be used to detect when over tting starts during supervised training of a neural network; training is then stopped before convergence to avoid the overtting (\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 selecti...
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Cross-entropy (CE)-based stopping criteria for turbo iterative decoding are known to outperform fixed-iteration stopping criteria at high signal-to-noise ratios (SNRs). While CE-based stopping criteria have a range of thresholds, a highvalue threshold for small frame sizes, and vice versa, should be used. It is difficult to advocate the value that can be categorized as either a small or large f...
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ژورنال
عنوان ژورنال: Neural Networks
سال: 1998
ISSN: 0893-6080
DOI: 10.1016/s0893-6080(98)00010-0