The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Extreme learning machines: a survey
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
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
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Self-citationLeast Squares Support Vector Machine Classifiers
Suykens, J. A. K., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,362
Cited by
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284
Extreme Learning Machines [Trends & Controversies]
Cambria, E., Huang, G.-B., Kasun, L. L. C., Zhou, H., Vong, C. M., Lin, J., Yin, J., Cai, Z., Liu, Q., Li, K., et al. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.140 · Cited by 710
Self-citationExtreme learning machine: algorithm, theory and applications
Ding, S., Zhao, H., Zhang, Y., Xu, X., & Nie, R. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9405-z · Cited by 593
A survey towards an integration of big data analytics to big insights for value-creation
Saggi, M. K., & Jain, S. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.01.010 · Cited by 494
Metadata
- Authors
- Huang, G.-B., Wang, D. H., & Lan, Y.
- Venue
- International Journal of Machine Learning and Cybernetics
- Published
- 2011-05-25
- Volume
- 2
- Issue
- 2
- Pages
- 107-122
- ISSN
- 1868-8071 (ISSN-L)
- 1868-808X
- OpenAlex
- W1993717606
- Cited by
- 1,914
- References
- 95
- FWCI
- 128.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Huang, G.-B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-011-0019-y
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.
