The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Approximation Methods for Gaussian Process Regression
Open accessAbstract
A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods. 1
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
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Kabzan, J., Hewing, L., Liniger, A., & Zeilinger, M. N. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2926677 · Cited by 462
Wabersich, K. P., Taylor, A. J., Choi, J. J., Sreenath, K., Tomlin, C. J., Ames, A. D., & Zeilinger, M. N. (2023) · IEEE Control Systems · doi:10.1109/mcs.2023.3291885 · Cited by 132
Metadata
- Authors
- Quiñonero-Candela, J., Rasmussen, C. E., & Williams, C. K. I.
- Venue
- Large-Scale Kernel Machines
- Publisher
- The MIT Press
- Published
- 2007-08-17
- Pages
- 203-224
- OpenAlex
- W2130819374
- Cited by
- 179
- References
- 2
- FWCI
- 6.22
- Type
- book chapter
- Language
- en
- Open access
- green
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- Quiñonero-Candela, J., Rasmussen, C. E., & Williams, C. K. I. (2007). Approximation Methods for Gaussian Process Regression. Large-Scale Kernel Machines. https://doi.org/10.7551/mitpress/7496.003.0011
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