Scalable variational Gaussian process classification

Hensman, James and Matthews, Alexander G. and Ghahramani, Zoubin (2015) Scalable variational Gaussian process classification. Proceedings of Machine Learning Research, 38. pp. 351-360. ISSN 1938-7228

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Abstract

Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of Machine Learning Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
84399
Deposited By:
Deposited On:
27 Jan 2017 14:08
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 02:49