The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Weighted least squares support vector machines: robustness and sparse approximation
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
Cited by
Benchmarking Least Squares Support Vector Machine Classifiers
van Gestel, T., Suykens, J. A. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., de Moor, B., & Vandewalle, J. (2004) · Machine Learning · doi:10.1023/b:mach.0000008082.80494.e0 · Cited by 745
Self-citationFinancial time series forecasting using independent component analysis and support vector regression
Lu, C.-J., Lee, T.-S., & Chiu, C.-C. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.02.001 · Cited by 537
Methodology for long-term prediction of time series
Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., & Lendasse, A. (2007) · Neurocomputing · doi:10.1016/j.neucom.2006.06.015 · Cited by 438
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S., & Nehdi, M. L. (2018) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.09.004 · Cited by 434
Predicting future hourly residential electrical consumption: A machine learning case study
Edwards, R. E., New, J., & Parker, L. E. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.03.010 · Cited by 391
Metadata
- Authors
- Suykens, J. A. K., De Brabanter, J., Lukas, L., & Vandewalle, J.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2002-10
- Volume
- 48
- Issue
- 1-4
- Pages
- 85-105
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W1978996791
- Cited by
- 1,265
- References
- 4
- FWCI
- 22.08
- Type
- journal article
- Language
- en
- Open access
- green
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- Suykens, J. A. K., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. https://doi.org/10.1016/s0925-2312(01)00644-0
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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.
