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PubMed · 10953245

Bootstrapping neural networks.

J Franke·M H Neumann
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Abstract

Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called boot-strap, which is based on an imitation of the probabilistic structure of the data-generating process on the basis of the information provided by a given set of random observations. In this article we investigate this classical method in the context of artificial neural networks used for estimating a mapping from input to output space. We establish consistency results for bootstrap estimates of the distribution of parameter estimates.

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BibTeXRIS

J Franke, M H Neumann. 2000. Bootstrapping neural networks.. https://doi.org/10.1162/089976600300015204

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