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...

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Detalhes bibliográficos
Principais autores: Liu, Haitao, Cai, Jianfei, Ong, Yew-Soon, Wang, Yi
Outros Autores: School of Computer Science and Engineering
Formato: Journal Article
Idioma:English
Publicado em: 2020
Assuntos:
Acesso em linha:https://hdl.handle.net/10356/139619