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Sinopsis
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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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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Learning with Partially Labeled and Interdependent Data
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783319353906_new
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Learning with Partially Labeled and Interdependent Data
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Berlin Springer International Publishing Springer Okt 2016, 2016
ISBN 10: 331935390X
ISBN 13: 9783319353906
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783319353906
Cantidad disponible: 2 disponibles
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Learning with Partially Labeled and Interdependent Data
Publicado por
Springer International Publishing, 2016
ISBN 10: 331935390X
ISBN 13: 9783319353906
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 458602863
Cantidad disponible: Más de 20 disponibles
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Learning with Partially Labeled and Interdependent Data
Publicado por
Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 331935390X
ISBN 13: 9783319353906
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783319353906
Cantidad disponible: 2 disponibles
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Learning with Partially Labeled and Interdependent Data
Librería: Chiron Media, Wallingford, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9783319353906
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Learning with Partially Labeled and Interdependent Data
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Condición: New. SUPER FAST SHIPPING. Nº de ref. del artículo: 9783319353906
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Learning With Partially Labeled and Interdependent Data
Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. reprint edition. 119 pages. 9.25x6.10x0.28 inches. In Stock. Nº de ref. del artículo: x-331935390X
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Learning with Partially Labeled and Interdependent Data
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 120. Nº de ref. del artículo: 385687018
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Learning with Partially Labeled and Interdependent Data
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. 120. Nº de ref. del artículo: 26378184245
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Learning with Partially Labeled and Interdependent Data
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND pp. 120. Nº de ref. del artículo: 18378184255
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