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An Evolutionary Bootstarp Approach to Neural Network Pruning and Generalization

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Author Info

  • Le Baron, B.

Abstract

This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping is used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to search the large number of potential network architectures. The combination of these two methods creates a network estimation and selection procedure which finds parsimonious network structures which generalize well. The bootstrap methodology also allows for objective functions other than usual least squares, since it can estimate the in sample bias for any function. Examples are given for forecasting chaotic time series contaminated with noise.

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Bibliographic Info

Paper provided by Wisconsin Madison - Social Systems in its series Working papers with number 9718.

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Length: 15 pages
Date of creation: 1997
Date of revision:
Handle: RePEc:att:wimass:9718

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Postal: UNIVERSITY OF WISCONSIN MADISON, SOCIAL SYSTEMS RESEARCH INSTITUTE(S.S.R.I.), MADISON WISCONSIN 53706 U.S.A.

Related research

Keywords: STATISTICS ; TESTS ; EVALUATION;

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Cited by:
  1. McNelis, Paul & McAdam, Peter, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 0352, European Central Bank.

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