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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods: 807 (Studies in Computational Intelligence) - Tapa dura
Sinopsis
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.
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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.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods (Studies in Computational Intelligence, 807, Band 807) [Hardcover] Vluymans, Sarah
Publicado por
Springer, 2018
ISBN 10: 3030046621
ISBN 13: 9783030046620
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Librería: SpringBooks, Berlin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Very Good. 1. Auflage. unread, with some shelfwear. Nº de ref. del artículo: CEA-2307C-GEMSE-32-1000XS
Cantidad disponible: 1 disponibles
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Publicado por
Springer International Publishing, 2018
ISBN 10: 3030046621
ISBN 13: 9783030046620
Nuevo
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Gebunden. Condición: 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. Nº de ref. del artículo: 256051619
Cantidad disponible: Más de 20 disponibles
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Publicado por
Springer International Publishing Dez 2018, 2018
ISBN 10: 3030046621
ISBN 13: 9783030046620
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783030046620
Cantidad disponible: 2 disponibles
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Publicado por
Springer International Publishing, 2018
ISBN 10: 3030046621
ISBN 13: 9783030046620
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783030046620
Cantidad disponible: 1 disponibles
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods (Studies in Computational Intelligence, 807)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783030046620_new
Cantidad disponible: Más de 20 disponibles
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 34066522-n
Cantidad disponible: Más de 20 disponibles
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Publicado por
Springer International Publishing, Springer International Publishing Dez 2018, 2018
ISBN 10: 3030046621
ISBN 13: 9783030046620
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch. Nº de ref. del artículo: 9783030046620
Cantidad disponible: 2 disponibles
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 34066522
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 34066522
Cantidad disponible: Más de 20 disponibles
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 34066522-n
Cantidad disponible: Más de 20 disponibles