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Learning Partially Labeled Interdependent by Amini Massih Reza (29 results)
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Learning with Partially Labeled and Interdependent Data
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 47.87
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Learning With Partially Labeled and Interdependent Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Learning with Partially Labeled and Interdependent Data
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Learning With Partially Labeled and Interdependent Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Learning With Partially Labeled and Interdependent Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Learning With Partially Labeled and Interdependent Data
Published by Springer-Verlag New York Inc, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Language: English
£ 65.85
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Add to basketHardcover. Condition: Brand New. 2015 edition. 122 pages. 9.25x6.25x0.50 inches. In Stock.
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£ 64.20
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Add to basketPaperback. Condition: Brand New. reprint edition. 119 pages. 9.25x6.10x0.28 inches. In Stock.
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Learning with Partially Labeled and Interdependent Data
Published by Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 331935390X ISBN 13: 9783319353906
Language: English
£ 51.90
Convert currency£ 53.29 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: 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.
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£ 44.59
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Add to basketTaschenbuch. Condition: Neu. Learning with Partially Labeled and Interdependent Data | Massih-Reza Amini (u. a.) | Taschenbuch | Previously published in hardcover | xiii | Englisch | 2016 | Springer | EAN 9783319353906 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Learning With Partially Labeled and Interdependent Data
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Learning with Partially Labeled and Interdependent Data
Published by Berlin Springer International Publishing Springer Okt 2016, 2016
ISBN 10: 331935390X ISBN 13: 9783319353906
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
£ 48.13
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Add to basketTaschenbuch. Condition: 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. 106 pp. Englisch.
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£ 65.70
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Add to basketCondition: New. Print on Demand 114.
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Condition: New. PRINT ON DEMAND 114.
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£ 79.30
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Add to basketCondition: New. Print on Demand pp. 120.
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Learning with Partially Labeled and Interdependent Data
Published by Springer International Publishing, 2015
ISBN 10: 3319157256 ISBN 13: 9783319157252
Language: English
£ 43.52
Convert currency£ 42.80 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: 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.
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Learning with Partially Labeled and Interdependent Data
Published by Springer International Publishing, 2016
ISBN 10: 331935390X ISBN 13: 9783319353906
Language: English
£ 43.85
Convert currency£ 42.80 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: 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.
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Condition: New. PRINT ON DEMAND pp. 120.
