9783319065984 - astronomy and big data: a data clustering approach to identifying uncertain galaxy morphology: 6 di edwards, kieran jay; gaber, mohamed medhat (17 risultati)
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Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, , GermaniaUniversitätsbuchhandlung Herta Hold GmbH
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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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EUR 115,62
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EUR 132,98
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Condizione: New. pp. xii + 105.
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 2014 edition. 103 pages. 9.25x6.25x0.50 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing 2014
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 106,99
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Buch. Condizione: 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 wisd…om 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.
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Da: Buchpark, Trebbin, , GermaniaBuchpark
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Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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 t…he 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.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: As New. Unread book in perfect condition.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Condizione: As New. Unread book in perfect condition.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 106,99
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch.
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Da: moluna, Greven, , Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent applications of Big Data research to AstronomyDemonstrates the application of Big data to the Galaxy Zoo project, where a large collection of galaxy images are annotated by citizen scientistsPresents…a Data Clustering Approa.
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Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
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Condizione: New. Print on Demand pp. xii + 105 54 Illus. (24 Col.).
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. PRINT ON DEMAND pp. xii + 105.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. Seek…ing 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 120 pp. Englisch.
