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  Estimating Predictive Variances with Kernel Ridge Regression

Cawley, G., Talbot, N., & Chapelle, O. (2006). Estimating Predictive Variances with Kernel Ridge Regression. In J. Quinonero-Candela, I. Dagan, B. Magnini, & F. D‘Alché-Buc (Eds.), Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005 (pp. 56-77). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D237-D Version Permalink: https://hdl.handle.net/21.11116/0000-0004-9C2A-E
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F11736790_5.pdf (Publisher version)
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 Creators:
Cawley, GC, Author
Talbot, NLC, Author
Chapelle, O1, 2, 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: In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the
predictive uncertainty is also required. There are two principal sources
of this uncertainty: the noise process contaminating the data and the
uncertainty in estimating the model parameters based on a limited sample
of training data. Both of them can be summarised in the predictive
variance which can then be used to give confidence intervals. In this paper,
we present various schemes for providing predictive variances for
kernel ridge regression, especially in the case of a heteroscedastic regression,
where the variance of the noise process contaminating the data is
a smooth function of the explanatory variables. The use of leave-one-out
cross-validation is shown to eliminate the bias inherent in estimates of
the predictive variance. Results obtained on all three regression tasks
comprising the predictive uncertainty challenge demonstrate the value
of this approach.

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 Dates: 2006-04
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/11736790_5
BibTex Citekey: 3643
 Degree: -

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Title: First PASCAL Machine Learning Challenges Workshop (MLCW 2005)
Place of Event: Southampton, UK
Start-/End Date: 2005-04-11 - 2005-04-13

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Title: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment: First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005
Source Genre: Proceedings
 Creator(s):
Quinonero-Candela, J, Editor
Dagan, I, Editor
Magnini, B, Editor
D‘Alché-Buc, F, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 56 - 77 Identifier: ISBN: 978-3-540-33427-9

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 3499 Sequence Number: - Start / End Page: - Identifier: -