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Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case., , , , , , и . DAMDID/RCDL (Selected Papers), том 822 из Communications in Computer and Information Science, стр. 61-72. Springer, (2017)Machine learning based data mining for Milky Way filamentary structures reconstruction., , , , , , , , , и . CoRR, (2015)Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys, , , , , , , , , и 10 other автор(ы). (2015)cite arxiv:1509.03318Comment: ApJ accepted, 17 pages, 10 figures.Neural Gas Based Classification of Globular Clusters., , , , , и . DAMDID/RCDL (Selected Papers), том 822 из Communications in Computer and Information Science, стр. 86-101. Springer, (2017)METAPHOR: Probability density estimation for machine learning based photometric redshifts., , , , , и . Astroinformatics, том 12 из Proceedings of the International Astronomical Union, стр. 197-200. Cambridge University Press, (2016)The LSST-DESC 3x2pt Tomography Optimization Challenge, , , , , , , , , и 18 other автор(ы). (2021)cite arxiv:2108.13418Comment: 30 pages (incl. 12 in appendix), 12 figures.stellar formation rates in galaxies using machine learning models., , , , и . ESANN, (2018)The Astronomical Data Deluge: the Template Case of Photometric Redshifts., , и . DAMDID/RCDL, том 2022 из CEUR Workshop Proceedings, стр. 27-29. CEUR-WS.org, (2017)Integrated data access, visualization and analysis for Galactic Plane surveys: the VIALACTEA case., , , , , , , , , и 1 other автор(ы). Astroinformatics, том 12 из Proceedings of the International Astronomical Union, стр. 291-298. Cambridge University Press, (2016)Cooperative photometric redshift estimation., , , , , , , и . Astroinformatics, том 12 из Proceedings of the International Astronomical Union, стр. 166-172. Cambridge University Press, (2016)