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Proceedings Paper

Deep learning classifier based on NPCA and orthogonal feature selection
Author(s): Stanisław Jankowski; Zbigniew Szymański; Uladzimir Dziomin; Vladimir Golovko; Aleksy Barcz
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Paper Abstract

In this paper the idea of deep learning classifier is developed. The effectiveness of discriminative classifier, as e.g. multilayer perceptron, support vector machine can be improved by adding the data preprocessing blocks: orthogonal feature selection (Gram-Schmidt method) and nonlinear principal component analysis. We present the case study of various structures of deep learning systems (scenarios).

Paper Details

Date Published: 28 September 2016
PDF: 9 pages
Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100315E (28 September 2016); doi: 10.1117/12.2249848
Show Author Affiliations
Stanisław Jankowski, Warsaw Univ. of Technology (Poland)
Zbigniew Szymański, Warsaw Univ. of Technology (Poland)
Uladzimir Dziomin, Brest State Technical Univ. (Belarus)
Vladimir Golovko, Brest State Technical Univ. (Belarus)
Aleksy Barcz, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10031:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016
Ryszard S. Romaniuk, Editor(s)

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