CUED Publications database

Scalable variational Gaussian process classification

Hensman, J and Matthews, AG and Ghahramani, Z (2015) Scalable variational Gaussian process classification. Journal of Machine Learning Research, 38. pp. 351-360. ISSN 1532-4435

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Abstract

Copyright 2015 by the authors. 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: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:44
Last Modified: 17 Nov 2017 20:19
DOI: