Multiple Instance Learning - Foundations and Algorithms (Paperback, Softcover reprint of the original 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	June 2018
First published:	2016
Authors:	Francisco Herrera • Sebastian Ventura • Rafael Bello • Chris Cornelis • Amelia Zafra • Danel Sanchez-Tarrago • Sarah Vluymans
Dimensions:	235 x 155 x 13mm (L x W x T)
Format:	Paperback
Pages:	233
Edition:	Softcover reprint of the original 1st ed. 2016
ISBN-13:	978-3-319-83815-1
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	3-319-83815-6
Barcode:	9783319838151

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