Open AccessProceedings Article
Scalable Variational Gaussian Process Classification
James Hensman,Alexander G. de G. Matthews,Zoubin Ghahramani +2 more
- 21 Feb 2015
- pp 351-360
TL;DR: This work shows how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets, and can be exploited to allow classification in problems with millions of data points.
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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.
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