Authors:
Offers a comprehensive overview of multiple instance learning widely used to classify and label texts, pictures, videos and music in the Internet
Provides the user with the most relevant algorithms for MIL and the most representative applications
Covers both the background and future directions of the field
Includes supplementary material: sn.pub/extras
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Table of contents (10 chapters)
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Front Matter
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Back Matter
About this book
This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined.
Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously.
This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.
Keywords
- Machine learning
- Data mining
- Multiple instance learning
- Multiple instance classification
- Multiple instance regression
- Multiple instance clustering
- Instance selection in multiple instance learning
- Dimensionality reduction
- Feature selection in multiple instance learning
- Multi-instance learning from imbalanced data
- Data reduction in multiple instance learning
- Multi-instance multi-label classification
- algorithm analysis and problem complexity
Authors and Affiliations
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Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Francisco Herrera
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Department of Computer Science, University of Córdoba, Córdoba, Spain
Sebastián Ventura
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Center of Information Studies, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
Rafael Bello
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Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
Chris Cornelis, Sarah Vluymans
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Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain
Amelia Zafra
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Central University "Marta Abreu" of La Villas, Santa Clara, Cuba
Dánel Sánchez-Tarragó
Bibliographic Information
Book Title: Multiple Instance Learning
Book Subtitle: Foundations and Algorithms
Authors: Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, Sarah Vluymans
DOI: https://doi.org/10.1007/978-3-319-47759-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Hardcover ISBN: 978-3-319-47758-9Published: 17 November 2016
Softcover ISBN: 978-3-319-83815-1Published: 29 June 2018
eBook ISBN: 978-3-319-47759-6Published: 08 November 2016
Edition Number: 1
Number of Pages: XI, 233
Number of Illustrations: 6 b/w illustrations, 40 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Algorithm Analysis and Problem Complexity