Published by Cham, Springer International Publishing., 2014
ISBN 10: 331906598X ISBN 13: 9783319065984
Language: English
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
Aufl. 2014. XII, 103 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped. Studies in Big Data ; 6. Sprache: Englisch.
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Published by Springer International Publishing, 2014
ISBN 10: 331906598X ISBN 13: 9783319065984
Language: English
Seller: Buchpark, Trebbin, Germany
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: Chiron Media, Wallingford, United Kingdom
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Published by Springer-Verlag New York Inc, 2014
ISBN 10: 331906598X ISBN 13: 9783319065984
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 2014 edition. 103 pages. 9.25x6.25x0.50 inches. In Stock.
Taschenbuch. Condition: Neu. Astronomy and Big Data | A Data Clustering Approach to Identifying Uncertain Galaxy Morphology | Kieran Edwards (u. a.) | Taschenbuch | Previously published in hardcover | xii | Englisch | 2016 | Springer | EAN 9783319383286 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Published by Springer-Verlag New York Inc, 2016
ISBN 10: 3319383280 ISBN 13: 9783319383286
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. reprint edition. 120 pages. 9.25x6.10x0.29 inches. In Stock.
Published by Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 3319383280 ISBN 13: 9783319383286
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as 'Uncertain'.This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND pp. 105.