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Da: Ria Christie Collections, Uxbridge, Regno Unito
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 60,42
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,67
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Condizione: New. pp. 120 Softcover reprint of the original 1st ed. 2015 edition NO-PA16APR2015-KAP.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 60,40
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 65,39
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Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Da: Revaluation Books, Exeter, Regno Unito
EUR 77,66
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Aggiungi al carrelloHardcover. Condizione: Brand New. 2015 edition. 122 pages. 9.25x6.25x0.50 inches. In Stock.
EUR 75,74
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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 119 pages. 9.25x6.10x0.28 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks.Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data.Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 116,55
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 107,04
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Aggiungi al carrelloPaperback. Condizione: Like New. Like New. book.
Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer International Publishing Mai 2015, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 53,49
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks.Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data.Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning. 120 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 74,43
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. 120.
Da: Majestic Books, Hounslow, Regno Unito
EUR 74,73
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Aggiungi al carrelloCondizione: New. Print on Demand 114.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 75,58
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 120.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 75,76
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND 114.
Lingua: Inglese
Editore: Springer International Publishing, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Da: moluna, Greven, Germania
EUR 48,37
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents an overview of statistical learning theoryAnalyzes two machine learning frameworks, semi-supervised learning with partially labeled data and learning with interdependent data Outlines how these frameworks can support emerging machi.
Lingua: Inglese
Editore: Springer International Publishing, 2016
ISBN 10: 331935390X ISBN 13: 9783319353906
Da: moluna, Greven, Germania
EUR 48,74
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents an overview of statistical learning theoryAnalyzes two machine learning frameworks, semi-supervised learning with partially labeled data and learning with interdependent data Outlines how these frameworks can support emerging machi.