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Machine Learning Paradigms: Applications in Recommender Systems: 92 (Intelligent Systems Reference Library) - Tapa blanda
Sinopsis
This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems.
The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.
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This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems.
The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.
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Machine Learning Paradigms
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Springer International Publishing, 2016
ISBN 10: 3319384961
ISBN 13: 9783319384962
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent applications of Recommender SystemsIntended for both the expert and researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader who wishes to learn more about the eme. Nº de ref. del artículo: 458605950
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Machine Learning Paradigms
Publicado por
Berlin Springer International Publishing Springer Okt 2016, 2016
ISBN 10: 3319384961
ISBN 13: 9783319384962
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in 'big data' as well as 'sparse data' problems.The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely. 125 pp. Englisch. Nº de ref. del artículo: 9783319384962
Cantidad disponible: 2 disponibles
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library, 92)
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Machine Learning Paradigms : Applications in Recommender Systems
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: 28123645-n
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Machine Learning Paradigms : Applications in Recommender Systems
Publicado por
Springer, Berlin, Springer International Publishing, Springer, 2016
ISBN 10: 3319384961
ISBN 13: 9783319384962
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 timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in 'big data' as well as 'sparse data' problems.The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely. Nº de ref. del artículo: 9783319384962
Cantidad disponible: 2 disponibles
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Machine Learning Paradigms : Applications in Recommender Systems
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library)
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. 125. Nº de ref. del artículo: 26375554735
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 125. Nº de ref. del artículo: 370490736
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Machine Learning Paradigms: Applications in Recommender Systems
Publicado por
Springer-Verlag New York Inc, 2016
ISBN 10: 3319384961
ISBN 13: 9783319384962
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Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. reprint edition. 140 pages. 9.25x6.10x0.34 inches. In Stock. Nº de ref. del artículo: x-3319384961
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Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library)
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 125. Nº de ref. del artículo: 18375554725
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