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Fast methods for training Gaussian processes on large datasets
Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing with large datasets. Here, we derive some simple...
Tallennettuna:
| Julkaisussa: | R Soc Open Sci |
|---|---|
| Päätekijät: | , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
The Royal Society Publishing
2016
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4892455/ https://ncbi.nlm.nih.gov/pubmed/27293793 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1098/rsos.160125 |
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