The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
A comprehensive evaluation of random vector functional link networks
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,155
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
Advances in Neural Information Processing Systems 19
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Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,459
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Handbook of Parametric and Nonparametric Statistical Procedures
(2004) · Technometrics · doi:10.1198/tech.2004.s209 · Cited by 4,661
Cited by
A review on neural networks with random weights
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.040 · Cited by 466
Prediction of surface roughness in extrusion-based additive manufacturing with machine learning
Li, Z., Zhang, Z., Shi, J., & Wu, D. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.01.004 · Cited by 432
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Distilling the Knowledge From Handcrafted Features for Human Activity Recognition
Chen, Z., Zhang, L., Cao, Z., & Guo, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2789925 · Cited by 160
Self-citation
Metadata
- Authors
- Zhang, L., & Suganthan, P. N.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 367-368
- Pages
- 1094-1105
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2138383519
- Cited by
- 465
- References
- 45
- FWCI
- 29.72
- 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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- Zhang, L., & Suganthan, P. N. (2016). A comprehensive evaluation of random vector functional link networks. Information Sciences. https://doi.org/10.1016/j.ins.2015.09.025
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.
