The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Network information criterion-determining the number of hidden units for an artificial neural network model
Abstract
The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.
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Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Cited by
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Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
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Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Hydrological modelling using artificial neural networks
Dawson, C. W., & Wilby, R. L. (2001) · Progress in Physical Geography: Earth and Environment · doi:10.1177/030913330102500104 · Cited by 903
Metadata
- Authors
- Murata, N., Yoshizawa, S., & Amari, S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1994
- Volume
- 5
- Issue
- 6
- Pages
- 865-872
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2098545770
- Cited by
- 645
- References
- 1
- FWCI
- 20.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Murata, N., Yoshizawa, S., & Amari, S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.329683
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