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Splitting Gaussian processes for computationally-efficient regression
Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian p...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384217/ https://www.ncbi.nlm.nih.gov/pubmed/34428233 http://dx.doi.org/10.1371/journal.pone.0256470 |