An Evolutionary Bootstarp Approach to Neural Network Pruning and Generalization
AbstractThis 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.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Wisconsin Madison - Social Systems in its series Working papers with number 9718.
Length: 15 pages
Date of creation: 1997
Date of revision:
Contact details of provider:
Postal: UNIVERSITY OF WISCONSIN MADISON, SOCIAL SYSTEMS RESEARCH INSTITUTE(S.S.R.I.), MADISON WISCONSIN 53706 U.S.A.
STATISTICS ; TESTS ; EVALUATION;
Find related papers by JEL classification:
- C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- McNelis, Paul & McAdam, Peter, 2004.
"Forecasting inflation with thick models and neural networks,"
Working Paper Series
0352, European Central Bank.
- McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ailsenne Sumwalt).
If references are entirely missing, you can add them using this form.