XFEL diffraction patterns representation method for classification, indexing and search

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The paper presents the results of application of machine learning methods: principle component analysis and support vector machine for classification of diffraction images produced in experiments at free-electron lasers. High efficiency of this approach presented by application to simulated data of adenovirus capsid and bluetongue virus core. This dataset were simulated with taking into account the real conditions of the experiment on lasers free electrons such as noise and features of used detectors.

Keywords: principle component analysis, support vector machine, coherent diffraction imaging
Citation in English: Bobkov S.A., Teslyuk A.B., Gorobtsov O.Yu., Yefanov O.M., Kurta R.P., Ilyin V.A., Golosova M.V., Vartanyants I.A. XFEL diffraction patterns representation method for classification, indexing and search // Computer Research and Modeling, 2015, vol. 7, no. 3, pp. 631-639

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International Interdisciplinary Conference "Mathematics. Computing. Education"

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