An Approximate Analytical Approach to Resampling Averages
Malzahn, Doerthe and Opper, Manfred (2003) An Approximate Analytical Approach to Resampling Averages. Journal of Machine Learning Research, 4, 1151-1173.
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Using a novel reformulation, we develop a framework to compute approximate resampling data averages analytically. The method avoids multiple retraining of statistical models on the samples. Our approach uses a combination of the replica ``trick'' of Statistical Physics and the TAP approach for approximate Bayesian inference. We demonstrate our approach on regression with Gaussian processes. A comparison with averages obtained by Monte-Carlo sampling shows that our method achieves good accuracy.
|Keywords:||bootstrap, kernel machines, Gaussian processes, approximate inference, statistical physics|
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science
|Date Deposited:||14 Mar 2004|
|Last Modified:||01 Mar 2012 10:59|
|Contributors:||Malzahn, Doerthe (Author)
Opper, Manfred (Author)
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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