An Evolutionary Bootstarp Approach to Neural Network Pruning and Generalization

B. Le Baron
Authors registered in the RePEc Author Service: Blake Lebaron ()

Working papers from Wisconsin Madison - Social Systems

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.

Keywords: STATISTICS; TESTS; EVALUATION (search for similar items in EconPapers)
JEL-codes: C50 C51 (search for similar items in EconPapers)
Pages: 15 pages
Date: 1997
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:att:wimass:9718

Access Statistics for this paper

More papers in Working papers from Wisconsin Madison - Social Systems UNIVERSITY OF WISCONSIN MADISON, SOCIAL SYSTEMS RESEARCH INSTITUTE(S.S.R.I.), MADISON WISCONSIN 53706 U.S.A..
Bibliographic data for series maintained by Ailsenne Sumwalt ().