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Vluymans Sarah (24 results)
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Condition: As New. Unread book in perfect condition.
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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)
Book 312 of 538: Studies in Computational IntelligenceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 94.30
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Multiple Instance Learning: Foundations and Algorithms
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 94.30
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New.
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Condition: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.
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Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 107.80
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Hardcover. Condition: Brand New. 248 pages. 9.25x6.10x0.79 inches. In Stock.
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Hardcover. Condition: Brand New. 267 pages. 9.25x6.10x0.83 inches. In Stock.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer International Publishing, Springer International Publishing Dez 2018, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer International Publishing, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Buch. Condition: 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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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer International Publishing Dez 2018, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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.
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Condition: New. Print on Demand.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods
Book 312 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer International Publishing, 2018
ISBN 10: 3030046621 ISBN 13: 9783030046620
Gebunden. Condition: 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.
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Multiple Instance Learning
Language: English
Published by Springer International Publishing, 2018
ISBN 10: 3319838156 ISBN 13: 9783319838151
Condition: 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.
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Multiple Instance Learning
Language: English
Published by Springer International Publishing, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Gebunden. Condition: 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.
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Condition: New. PRINT ON DEMAND.
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Buch. Condition: Neu. Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods | Sarah Vluymans | Buch | xviii | Englisch | 2018 | Springer | EAN 9783030046620 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
