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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods Hardback - 2018

by Sarah Vluymans

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Reader reviews for Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods

From the publisher

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.

From the rear cover

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.

Details

  • Title Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods
  • Author Sarah Vluymans
  • Binding Hardback
  • Pages 249
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2018-12-05
  • Illustrated Yes
  • Features Bibliography, Illustrated
  • ISBN 9783030046620 / 3030046621
  • Weight 1.22 lbs (0.55 kg)
  • Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
  • Category Computers - General Information
  • Dewey Decimal Code 006.3

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...

Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

by Sarah Vluymans

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; 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 mathemat
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

by Vluymans, Sarah (Author)

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Springer, 2019. Hardcover. New. 267 pages. 9.25x6.10x0.83 inches.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

by Sarah Vluymans

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...
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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)

by Vluymans, Sarah

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hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...
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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods

by Sarah Vluymans

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Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set...
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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)

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ISBN 10 / ISBN 13
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Springer , 1st ed. 2019 edition NO-PA16APR2015-KAP. Hardback. New.
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