The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Artificial neural networks as a tool in ecological modelling, an introduction
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Citations
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
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 2×
“This procedure of partitioning the data is called k-fold cross-validation, some times named the ho1d-out procedure (Utans and Moody, 1991; Geman et al., 1992; Efron and Tibshirani, 1995; Kohavi, 1995; Kohavi and Wolpert, 1996; Friedman, 1997).”
Cited by
Sokolova, I. M. (2013) · Integrative and Comparative Biology · doi:10.1093/icb/ict028 · Cited by 719
The application of artificial neural networks to the analysis of remotely sensed data
Mas, J. F., & Flores, J. J. (2008) · International Journal of Remote Sensing · doi:10.1080/01431160701352154 · Cited by 616
Ahmadi-Nedushan, B., St-Hilaire, A., Bérubé, M., Robichaud, É., Thiémonge, N., & Bobée, B. (2006) · River Research and Applications · doi:10.1002/rra.918 · Cited by 320
Citizens as consumers: Profiling e-government services’ users in Egypt via data mining techniques
Mostafa, M. M., & El-Masry, A. A. (2013) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2013.03.007 · Cited by 60
Metadata
- Authors
- Lek, S., & Guégan, J. F.
- Venue
- Ecological Modelling
- Publisher
- Elsevier BV
- Published
- 1999-08
- Volume
- 120
- Issue
- 2-3
- Pages
- 65-73
- ISSN
- 0304-3800 (ISSN-L)
- OpenAlex
- W2000532370
- Cited by
- 889
- References
- 11
- FWCI
- 12.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Artificial neural networks as a tool in ecological modelling, an introduction. https://app.cochat.ai/papers/10.1016/s0304-3800(99)00092-7
- The paper
- Lek, S., & Guégan, J. F. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling. https://doi.org/10.1016/s0304-3800(99)00092-7
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.
