Pareto selection of neural network approximation subject to virtual leave-one-out criteria and application to defect centers identification in semi-insulating materials

Stanislaw Jankowski; Maciej Ojczyk

doi:10.1117/12.674865

26 April 2006 Pareto selection of neural network approximation subject to virtual leave-one-out criteria and application to defect centers identification in semi-insulating materials

Stanislaw Jankowski, Maciej Ojczyk

Author Affiliations +

Proceedings Volume 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV; 615930 (2006) https://doi.org/10.1117/12.674865
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, 2005, Wilga, Poland

Abstract

Selection of neural networks for function approximation are well known and widely described in many recent papers. This study extends the understanding of the problem on different areas of optimization. Typically selection of best model reduces to searching for models that best fit to leave-one-out criteria. This work joins leave-one-out criteria with genetic algorithms optimization methods and implements it with respect to Pareto optimum. Algorithm constructed in this study basis on presented methods and was applied in semi-insulating materials approximation problem as well as synthetic data models selection.

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Stanislaw Jankowski and Maciej Ojczyk "Pareto selection of neural network approximation subject to virtual leave-one-out criteria and application to defect centers identification in semi-insulating materials", Proc. SPIE 6159, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, 615930 (26 April 2006); https://doi.org/10.1117/12.674865

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