Support vector machine for EELS oxidation state determination-Reference-Cited by-同舟云学术

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Support vector machine for EELS oxidation state determination

Published:2021-02 Issue: Volume:221 Page:113190
ISSN:0304-3991
Container-title:Ultramicroscopy
language:en
Short-container-title:Ultramicroscopy

Author:

del-Pozo-Bueno D.,Peiró F.,Estradé S.

Funder

Ministerio de Ciencia, Innovación y Universidades

Publisher

Elsevier BV

Subject

Instrumentation,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference27 articles.

1. Mapping chemical and bonding information using multivariate analysis of electron energy-loss spectrum images;Bosman;Ultramicroscopy,2006

2. Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis;Bonnet;Ultramicroscopy,1999

3. Mapping titanium and tin oxide phases using EELS: An application of independent component analysis;de la Peña;Ultramicroscopy,2011

4. Spectral mixture analysis of EELS spectrum-images;Dobigeon;Ultramicroscopy,2012

5. Clustering analysis strategies for electron energy loss spectroscopy (EELS);Torruella;Ultramicroscopy,2018

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2. Combining low-loss EELS experiments with machine learning-based algorithms to automate the phases separation imaging in industrial duplex stainless steels;Materials Characterization;2024-05

3. Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks;Microscopy and Microanalysis;2024-04

4. Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks;Ultramicroscopy;2023-11

5. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy;Scientific Reports;2023-08-22

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