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  Assessing Approximations for Gaussian Process Classification

Kuss, M., & Rasmussen, C. (2006). Assessing Approximations for Gaussian Process Classification. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 699-706.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D1DF-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D1E0-6
Genre: Conference Paper

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 Creators:
Kuss, M1, Author           
Rasmussen, CE1, Author           
Weiss, Editor
Y., Editor
Schölkopf, B., Editor
Platt, J., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplaceamp;amp;amp;amp;amp;amp;amp;amp;lsquo;s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.

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 Dates: 2006-05
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-23253-7
URI: http://nips.cc/Conferences/2005/
BibTex Citekey: 3530
 Degree: -

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Title: Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005)
Place of Event: Whistler, BC, Canada
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 699 - 706 Identifier: -