Multiple Instance Learning Foundations and Algorithms /

• Κύριοι συγγραφείς: Herrera, Francisco (Συγγραφέας), Ventura, Sebastián (Συγγραφέας), Bello, Rafael (Συγγραφέας), Cornelis, Chris (Συγγραφέας), Zafra, Amelia (Συγγραφέας), Sánchez-Tarragó, Dánel (Συγγραφέας), Vluymans, Sarah (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
• Θέματα: 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...

Πίνακας περιεχομένων:

• 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.