The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Support Vector Regression
Abstract
Chapter 9 presents support vector regression (SVR), a relatively newer supervised learning algorithm for predictive regression modeling, which – like random forests for regression – also may outperform the least-squares - based methods. Discussed is ε -insensitive loss used by SVR, the ε -tube concept, as well as algorithms for linear and nonlinear SVRs.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
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Prediction of Sea Surface Temperature Using Long Short-Term Memory
Zhang, Q., Wang, H., Dong, J., Zhong, G., & Sun, X. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2733548 · Cited by 482
Zhu, B., Han, D., Wang, P., Wu, Z., Zhang, T., & Wei, Y.-M. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.076 · Cited by 307
Han, I.-S., & Chung, C.-B. (2016) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2016.04.247 · Cited by 141
Prediction of arrival times of freight traffic on US railroads using support vector regression
Barbour, W., Martinez Mori, J. C., Kuppa, S., & Work, D. B. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.05.019 · Cited by 116
Metadata
- Venue
- Multivariate Biomarker Discovery
- Publisher
- Cambridge University Press
- Published
- 2024-06-06
- Pages
- 136-146
- OpenAlex
- W2291445757
- Cited by
- 809
- References
- 1
- FWCI
- 0.68
- Type
- book chapter
- Language
- en
- Open access
- closed
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