The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Optimal unsupervised learning in a single-layer linear feedforward neural network
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,923
Advanced engineering mathematics
(1998) · Choice Reviews Online · doi:10.5860/choice.35-5129 · Cited by 4,100
Self-organized formation of topographically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · Cited by 2,995
Cited by
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Sherstinsky, A. (2020) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2019.132306 · Cited by 5,680
Nonlinear principal component analysis using autoassociative neural networks
Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
Metadata
- Authors
- Sanger, T. D.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 1989-01
- Volume
- 2
- Issue
- 6
- Pages
- 459-473
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2131329059
- Cited by
- 1,509
- References
- 45
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Optimal unsupervised learning in a single-layer linear feedforward neural network. https://app.cochat.ai/papers/10.1016/0893-6080(89)90044-0
- The paper
- Sanger, T. D. (1989). Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks. https://doi.org/10.1016/0893-6080(89)90044-0
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.
