Understanding and comparing scalable Gaussian process regression for big data
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization. The cubic complexity of standard GP however leads to poor scalability, which poses challenges in the...
| Principais autores: | , , , |
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| Outros Autores: | |
| Formato: | Journal Article |
| Idioma: | English |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/139619 |