The DOI matches its Crossref record.
Least Squares Support Vector Machines
Abstract
Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS-SVM for Unsupervised Learning LS-SVM for Recurrent Networks and Control.
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Citations
Cited by
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Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Kernel-based methods for hyperspectral image classification
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Grant, M., Boyd, S., & Ye, Y. (2006) · Nonconvex Optimization and Its Applications · doi:10.1007/0-387-30528-9_7 · Cited by 1,026
Metadata
- Authors
- Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J.
- Venue
- WORLD SCIENTIFIC eBooks
- Publisher
- WORLD SCIENTIFIC
- Published
- 2002-11
- DOI
- 10.1142/5089
- OpenAlex
- W1496317909
- Cited by
- 3,642
- References
- 0
- FWCI
- 13.50
- Type
- book
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Least Squares Support Vector Machines. https://app.cochat.ai/papers/10.1142/5089
- The paper
- Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least Squares Support Vector Machines. WORLD SCIENTIFIC eBooks. https://doi.org/10.1142/5089
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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.
