12 October 2006 Approximation of HRPITS results for SI GaAs by large scale support vector machine algorithms
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Proceedings Volume 6347, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2006; 634730 (2006) https://doi.org/10.1117/12.714857
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2006, 2006, Wilga, Poland
Abstract
For the first time large-scale support vector machine algorithms are used to extraction defect parameters in semi-insulating (SI) GaAs from high resolution photoinduced transient spectroscopy experiment. By smart decomposition of the data set the SVNTorch algorithm enabled to obtain good approximation of analyzed correlation surface by a parsimonious model (with small number of support vector). The extracted parameters of deep level defect centers from SVM approximation are of good quality as compared to the reference data.
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Stanisław Jankowski, Konrad Wojdan, Zbigniew Szymański, Roman Kozłowski, "Approximation of HRPITS results for SI GaAs by large scale support vector machine algorithms", Proc. SPIE 6347, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2006, 634730 (12 October 2006); doi: 10.1117/12.714857; https://doi.org/10.1117/12.714857
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