Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Language: English
Published by Springer, 2018
Series: Book 312 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 104.50
Quantity: 1 available
Add to basketItem description from seller
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.…
Seller Inventory # 9783030046620
- Title
- Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
- Author
- Sarah Vluymans
- Publisher
- Springer
- Publication year
- 2018
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3030046621
- ISBN 13
- 9783030046620
- Item weight
- 571 grams
- Dimensions
- 241x160x21 mm
- Series
- Book 312 of 538: Studies in Computational Intelligence
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