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  Approximations for Binary Gaussian Process Classification

Nickisch, H., & Rasmussen, C. (2008). Approximations for Binary Gaussian Process Classification. The Journal of Machine Learning Research, 9, 2035-2078.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C69B-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-2CAC-B
Genre: Journal Article

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 Creators:
Nickisch, H1, 2, Author              
Rasmussen, CE, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2008-10
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 5305
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 2035 - 2078 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1