The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Essentials of the self-organizing map
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Improved tools for biological sequence comparison.
Pearson, W. R., & Lipman, D. J. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.8.2444 · Cited by 11,227
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Self-citationAn Algorithm for Vector Quantizer Design
Linde, Y., Buzo, A., & Gray, R. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094577 · Cited by 7,201
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
Gray, R. M. (1990) · Readings in Speech Recognition · doi:10.1016/b978-0-08-051584-7.50011-5 · Cited by 2,845
Cited by
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00815-1 · Cited by 2,678
Machine learning for data-driven discovery in solid Earth geoscience
Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019) · Science · doi:10.1126/science.aau0323 · Cited by 1,249
High‐Throughput Metabolomics by 1D NMR
Vignoli, A., Ghini, V., Meoni, G., Licari, C., Takis, P. G., Tenori, L., Turano, P., & Luchinat, C. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201804736 · Cited by 365
A feature learning approach based on XGBoost for driving assessment and risk prediction
Shi, X., Wong, Y. D., Li, M. Z.-F., Palanisamy, C., & Chai, C. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.05.005 · Cited by 357
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295
Metadata
- Authors
- Kohonen, T.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 37
- Pages
- 52-65
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2079810998
- Cited by
- 1,601
- References
- 7
- FWCI
- 41.16
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Essentials of the self-organizing map. https://app.cochat.ai/papers/10.1016/j.neunet.2012.09.018
- The paper
- Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks. https://doi.org/10.1016/j.neunet.2012.09.018
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.
