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GPTreeO: Dividing Local Gaussian Processes for Online Learning Regression
We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs.
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Package details |
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| Author | Timo Braun [aut, cre] (<https://orcid.org/0009-0001-0965-8285>), Anders Kvellestad [aut] (<https://orcid.org/0000-0002-5267-7705>), Riccardo De Bin [ctb] (<https://orcid.org/0000-0002-7441-6880>) |
| Maintainer | Timo Braun <gptreeo.timo.braun@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.0.1 |
| Package repository | View on CRAN |
| Installation |
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