The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Prediction Risk and Architecture Selection for Neural Networks
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Citations
Cites
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“A limited training set results in a more severe bias / variance (or underfitting vs overfitting) tradeoff (see for example Geman, Bienenstock and Doursat (1992) ), so the model selection problem is both more challenging and more crucial.”
BackgroundCraven, P., & Wahba, G. (1978) · Numerische Mathematik · doi:10.1007/bf01404567 · Cited by 3,388
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-0919-5_38 · Cited by 3,028
The Predictive Sample Reuse Method with Applications
Geisser, S. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10479865 · Cited by 2,748 · cited 4×
“For reviews, see Stone (1974; 1978), Geisser (1975), Eubank (1988), Hastie and Tibshirani (1990), and Wahba (1990).”
BackgroundMethodInfluentialDynamic node creation in backpropagation networks
ASH, T. (1989) · Connection Science · doi:10.1080/09540098908915647 · Cited by 415
Cited by
Catastrophic forgetting, rehearsal and pseudorehearsal
ROBINS, A. (1995) · Connection Science · doi:10.1080/09540099550039318 · Cited by 833
Kwok, T.-Y., & Yeung, D.-Y. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572102 · Cited by 493
Neural networks in geophysical applications
van der Baan, M., & Jutten, C. (2000) · Geophysics · doi:10.1190/1.1444797 · Cited by 342
Applications of artificial neural networks in chemical engineering
Himmelblau, D. M. (2000) · Korean Journal of Chemical Engineering · doi:10.1007/bf02706848 · Cited by 273
Metadata
- Authors
- Moody, J.
- Venue
- From Statistics to Neural Networks
- Publisher
- Springer Berlin Heidelberg
- Published
- 1994
- Pages
- 147-165
- OpenAlex
- W1481519903
- Cited by
- 159
- References
- 7
- FWCI
- 8.82
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Moody, J. (1994). Prediction Risk and Architecture Selection for Neural Networks. From Statistics to Neural Networks. https://doi.org/10.1007/978-3-642-79119-2_7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
