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- W3127347916 abstract "Several machine-learning algorithms were tested to identify blazar candidates among the 4FGL unassociated sources. Using the Scikit-learn toolkit, for a given set of discriminant parameters, methods based on random forests, multilayer perceptions, and support vector machines could reach comparable performance with a very little tuning. We obtain 510 blazar candidates, by combining the results of three classifiers." @default.
- W3127347916 created "2021-02-15" @default.
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- W3127347916 date "2020-12-11" @default.
- W3127347916 modified "2023-09-26" @default.
- W3127347916 title "Research and characterization of blazar candidates among the Fermi/LAT4FGL catalog using Machine Learning" @default.
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- W3127347916 doi "https://doi.org/10.1109/itaic49862.2020.9339103" @default.
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