Multiple Instance Learning: Foundations and Algorithms (Hardback)Francisco Herrera (author), Sebastian Ventura (author), Rafael Bello (author), Chris Cornelis (author), Amelia Zafra (author), Danel Sanchez-Tarrago (author), Sarah Vluymans (author)
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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.
Publisher: Springer International Publishing AG
Number of pages: 233
Weight: 537 g
Dimensions: 235 x 155 x 16 mm
Edition: 1st ed. 2016
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