The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Asymptotic statistical theory of overtraining and cross-validation
Abstract
A statistical theory for overtraining is proposed. The analysis treats general realizable stochastic neural networks, trained with Kullback-Leibler divergence in the asymptotic case of a large number of training examples. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Based on the cross-validation stopping we consider the ratio the examples should be divided into training and cross-validation sets in order to obtain the optimum performance. Although cross-validated early stopping is useless in the asymptotic region, it surely decreases the generalization error in the nonasymptotic region. Our large scale simulations done on a CM5 are in good agreement with our analytical findings.
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Introduction to multi-layer feed-forward neural networks
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Prechelt, L. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_5 · Cited by 951
Metadata
- Authors
- Amari, S., Murata, N., Muller, K.-R., Finke, M., & Yang, H. H.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1997-09
- Volume
- 8
- Issue
- 5
- Pages
- 985-996
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2141212940
- Cited by
- 351
- References
- 23
- FWCI
- 13.79
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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