Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 123,09
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 115,49
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 115,49
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 130,92
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 115,48
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 130,43
Quantità: 15 disponibili
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 128,62
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Condizione: New.
Condizione: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.
EUR 153,55
Quantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 248 pages. 9.25x6.10x0.79 inches. In Stock.
Da: Revaluation Books, Exeter, Regno Unito
EUR 153,87
Quantità: 2 disponibili
Aggiungi al carrelloHardcover. Condizione: Brand New. 267 pages. 9.25x6.10x0.83 inches. In Stock.
Condizione: New.
Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning.The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 177,61
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: New. New. book.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 86,24
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Dez 2018, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning.The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields. 268 pp. Englisch.
Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Da: moluna, Greven, Germania
EUR 92,27
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Takes the research on ordered weighted average (OWA) fuzzy rough sets to the next level Provides clear guidelines on how to use them Expands the application to e.g. imbalanced, semi-supervised, multi-instance, and multi-label clas.
Lingua: Inglese
Editore: Springer International Publishing, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Da: moluna, Greven, Germania
EUR 92,27
Quantità: Più di 20 disponibili
Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a comprehensive overview of multiple instance learning widely used to classify and label texts, pictures, videos and music in the InternetProvides the user with the most relevant algorithms for MIL and the most represen.
Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3319838156 ISBN 13: 9783319838151
Da: moluna, Greven, Germania
EUR 92,27
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a comprehensive overview of multiple instance learning widely used to classify and label texts, pictures, videos and music in the InternetProvides the user with the most relevant algorithms for MIL and the most represen.
Da: Majestic Books, Hounslow, Regno Unito
EUR 149,38
Quantità: 4 disponibili
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Da: Majestic Books, Hounslow, Regno Unito
EUR 149,65
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 149,49
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 149,78
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Dez 2018, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 268 pp. Englisch.