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  Gaussian Processes for Regression

Williams, C., & Rasmussen, C. (1996). Gaussian Processes for Regression. In D. Touretzky, M. Mozer, & M. hasselmo (Eds.), Advances in Neural Processing Systems 8 (pp. 514-520). Cambridge, MA, USA: MIT Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-EB66-9 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-DE84-F
Genre: Conference Paper

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https://papers.nips.cc/paper/1048-gaussian-processes-for-regression.pdf (Publisher version)
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 Creators:
Williams, CKI, Author
Rasmussen, CE1, Author           
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

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 Dates: 1996-06
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2468
 Degree: -

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Title: Ninth Annual Conference on Neural Information Processing Systems (NIPS 1995)
Place of Event: Denver, CO, USA
Start-/End Date: 1995-11-27 - 1995-12-02

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Title: Advances in Neural Processing Systems 8
Source Genre: Proceedings
 Creator(s):
Touretzky , DS, Editor
Mozer, MC, Editor
hasselmo, ME, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 514 - 520 Identifier: ISBN: 0-262-20107-0