Isbn: 9783030046620 - dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods: 807 (18 risultati)

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Condizione: New. In English.

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Condizione: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.

Hardcover. Condizione: Brand New. 267 pages. 9.25x6.10x0.83 inches. In Stock.

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Buch. 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.…

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Buch. 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.…

Gebunden. 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&nbspProvides clear guidelines on how to use them&nbspExpands the application to e.g. imbalanced, semi-supervised, multi-instance, and multi-label clas. …

Condizione: New. Print on Demand.

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Buch. 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.…