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Multiple Instance Learning

Foundations and Algorithms

Authors: Herrera, F., Ventura, S., Bello, R., Cornelis, C., Zafra, A., Sánchez-Tarragó, D., Vluymans, S.

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  • 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
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eBook 101,14 €
price for Spain (gross)
  • ISBN 978-3-319-47759-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-47758-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-83815-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included.
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.

Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook 101,14 €
price for Spain (gross)
  • ISBN 978-3-319-47759-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-47758-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-83815-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Multiple Instance Learning
Book Subtitle
Foundations and Algorithms
Authors
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-47759-6
DOI
10.1007/978-3-319-47759-6
Hardcover ISBN
978-3-319-47758-9
Softcover ISBN
978-3-319-83815-1
Edition Number
1
Number of Pages
XI, 233
Number of Illustrations
6 b/w illustrations, 40 illustrations in colour
Topics