An analysis of feature relevance in the classification of astronomical transients with machine learning methods
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BiBTeXSEEK ID: https://publications.h-its.org/publications/99
Research Groups: Astroinformatics
Publication type: Journal
Journal: Monthly Notices of the Royal Astronomical Society
Publisher: Oxford University Press
Citation: Monthly Notices of the Royal Astronomical Society, 457:3119–3132
Date Published: 2016
Registered Mode: imported from a bibtex file
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Created: 7th Sep 2019 at 07:35
Last updated: 5th Mar 2024 at 21:23
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Research Groups: Astroinformatics
Institutions: Heidelberg Institute for Theoretical Studies
Research Groups: Scientific Databases and Visualisation, Astroinformatics, Data Mining and Uncertainty Quantification, Groups and Geometry, Natural Language Processing, Computational Molecular Evolution, Computational Carbon Chemistry, Physics of Stellar Objects, Playground, Molecular and Cellular Modeling, Stellar Evolution Theory, SIMPLAIX
Institutions: Heidelberg Institute for Theoretical Studies
Public web page: Not specified
