Multiple Instance Learning

Foundations and Algorithms

Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó, Sarah Vluymans (Autoren)

Buch | Softcover

XI, 233 Seiten

2018 | 1. Softcover reprint of the original 1st ed. 2016
Springer International Publishing (Verlag)
978-3-319-83815-1 (ISBN)

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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.

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.

Erscheinungsdatum	05.03.2022
Zusatzinfo	XI, 233 p. 46 illus., 40 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	385 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Algorithm analysis and problem complexity • Data Mining • Data reduction in multiple instance learning • dimensionality reduction • Feature selection in multiple instance learning • Instance selection in multiple instance learning • machine learning • Multi-instance learning from imbalanced data • Multi-instance multi-label classification • Multiple instance classification • Multiple instance clustering • multiple instance learning • Multiple instance regression
ISBN-10	3-319-83815-6 / 3319838156
ISBN-13	978-3-319-83815-1 / 9783319838151
Zustand	Neuware