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A Simple Strategy to prune Neural Networks with an Application to Economic Time Series

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  • Johan F. Kaashoek
  • Herman K. van Dijk

    (Erasmus University Rotterdam)

Abstract

A major problem in applying neural networks is specifying the sizeof the network. Even for moderately sized networks the number ofparameters may become large compared to the number of data. In thispaper network performance is examined while reducing the size of thenetwork through the use of multiple correlation coefficients andgraphical analysis of network output per hidden layer cell andinput layer cell.

Suggested Citation

  • Johan F. Kaashoek & Herman K. van Dijk, 1997. "A Simple Strategy to prune Neural Networks with an Application to Economic Time Series," Tinbergen Institute Discussion Papers 97-123/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:19970123
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    1. Schotman, Peter C & van Dijk, Herman K, 1991. "On Bayesian Routes to Unit Roots," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 387-401, Oct.-Dec..
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    2. Kaashoek, J.F. & van Dijk, H.K., 1999. "Neural network analysis of varying trends in real exchange rates," Econometric Institute Research Papers EI 9915-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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