Hannes, N and C E, R (2008) Approximations for Binary Gaussian Process Classification. Journal of Machine Learning Research, 9. pp. 2035-2078. ISSN 1532-4435Full text not available from this repository.
We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.
|Uncontrolled Keywords:||Gaussian process priors, probabilistic classification, Laplaces’s approximation, expectation propagation, variational bounding, mean field methods, marginal likelihood evidence, MCMC|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||02 Sep 2016 16:41|
|Last Modified:||26 Sep 2016 07:57|