Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks.

Del-Pozo-Bueno, Daniel; Kepaptsoglou, Demie; Peiró, Francesca; Estradé, Sònia

Del-Pozo-Bueno, Daniel; Kepaptsoglou, Demie; Peiró, Francesca; Estradé, Sònia.

Afiliação

Del-Pozo-Bueno D; Departament d'Enginyeria Electrònica i Biomèdica, LENS-MIND, Universitat de Barcelona, Barcelona 08028, Spain; Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona, Barcelona 08028, Spain. Electronic address: dpb.523@gmail.com.
Kepaptsoglou D; SuperSTEM, Sci-Tech Daresbury Campus, Daresbury WA4 4AD, UK; School of Physics, Engineering and Technology, University of York, Heslington YO10 5DD, UK.
Peiró F; Departament d'Enginyeria Electrònica i Biomèdica, LENS-MIND, Universitat de Barcelona, Barcelona 08028, Spain; Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona, Barcelona 08028, Spain.
Estradé S; Departament d'Enginyeria Electrònica i Biomèdica, LENS-MIND, Universitat de Barcelona, Barcelona 08028, Spain; Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona, Barcelona 08028, Spain.

Ultramicroscopy ; 253: 113828, 2023 Nov.

Article em En | MEDLINE | ID: mdl-37556961

Palavras-chave

Artificial neural networks; Electron energy loss spectroscopy; Machine learning; Oxidation state; Support vector machines; Transition metals

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Aspecto: Determinantes_sociais_saude Idioma: En Revista: Ultramicroscopy Ano de publicação: 2023 Tipo de documento: Article

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