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Variable projections neural network training

Author

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  • Pereyra, V.
  • Scherer, G.
  • Wong, F.

Abstract

The training of some types of neural networks leads to separable non-linear least squares problems. These problems may be ill-conditioned and require special techniques. A robust algorithm based on the Variable Projections method of Golub and Pereyra is designed for a class of feed-forward neural networks and tested on benchmark examples and real data.

Suggested Citation

  • Pereyra, V. & Scherer, G. & Wong, F., 2006. "Variable projections neural network training," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 231-243.
    • Handle: RePEc:eee:matcom:v:73:y:2006:i:1:p:231-243
    DOI: 10.1016/j.matcom.2006.06.017
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    Cited by:

    1. Pereyra, Victor, 2009. "Fast computation of equispaced Pareto manifolds and Pareto fronts for multiobjective optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1935-1947.
    2. Alonso, J.J. & LeGresley, P. & Pereyra, V., 2009. "Aircraft design optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1948-1958.

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