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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. Machine Learning Challenges: First PASCAL Machine Learning Challenges Workshop (MLCW 2005), 56-77.

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 Creators:
Cawley, GC, Author
Talbot, NLC, Author
Chapelle, O1, Author           
Quinonero-Candela, Editor
J., Editor
Dagan, I., Editor
Magnini, B., Editor
D‘Alché-Buc, F., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: -
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Title: First PASCAL Machine Learning Challenges Workshop
Place of Event: Southampton, United Kingdom
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Title: Machine Learning Challenges: First PASCAL Machine Learning Challenges Workshop (MLCW 2005)
Source Genre: Journal
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 56 - 77 Identifier: -