Multiple Instance Learning Foundations and Algorithms /

• Main Authors: Herrera, Francisco (Author), Ventura, Sebastián (Author), Bello, Rafael (Author), Cornelis, Chris (Author), Zafra, Amelia (Author), Sánchez-Tarragó, Dánel (Author), Vluymans, Sarah (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Subjects: Computer science.; Algorithms.; Artificial intelligence.; Image processing.; Computer Science.; Artificial Intelligence (incl. Robotics).; Image Processing and Computer Vision.; Algorithm Analysis and Problem Complexity.

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...

Table of Contents:

• 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.