Herrera / Ventura / Bello Multiple Instance Learning
1. Auflage 2016
ISBN: 978-3-319-47759-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Foundations and Algorithms
E-Book, Englisch, 235 Seiten
ISBN: 978-3-319-47759-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Introduction.- Multiple Instance Learning.- Multi-Instance Classification.- Instance-Based Classification Methods.- Bag-Based Classification Methods.- Multi-Instance Regression.- Unsupervised Multiple Instance Learning.- Data Reduction.- Imbalance Multi-Instance Data.- Multiple Instance Multiple Label Learning.