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  Incremental Gaussian Processes

Quinonero Candela, J., & Winther, O. (2003). Incremental Gaussian Processes. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 1001-1008). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DB41-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6BD7-1
Genre: Conference Paper

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 Creators:
Quinonero Candela, J1, Author              
Winther, O, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: In this paper, we consider Tipping‘s relevance vector machine (RVM) and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call subspace EM. Working with a subset of active basis functions, the sparsity of the RVM solution will ensure that the number of basis functions and thereby the computational complexity is kept low. We also introduce a mean field approach to the intractable classification model that is expected to give a very good approximation to exact Bayesian inference and contains the Laplace approximation as a special case. We test the algorithms on two large data sets with O(10^3-10^4) examples. The results indicate that Bayesian learning of large data sets, e.g. the MNIST database is realistic.

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 Dates: 2003-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2800
 Degree: -

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Title: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2002-12-09 - 2002-12-14

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Title: Advances in Neural Information Processing Systems 15
Source Genre: Proceedings
 Creator(s):
Becker, S, Editor
Thrun, S, Editor
Obermayer, K, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1001 - 1008 Identifier: ISBN: 0-262-02550-7