Название: Multiview Machine Learning Автор: Shiliang Sun, Liang Mao
Издательство: Springer
ISBN: 981133028X
Год: 2019
Страниц: 149
Язык: английский
Формат: pdf (true), epub
Размер: 10.18 MB
This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.
During the past two decades, multiview learning as an emerging direction in machine learning became a prevailing research topic in artificial intelligence (AI). Its success and popularity were largely motivated by the fact that real-world applications generate various data as different views while people try to manipulate and integrate those data for performance improvements. In the data era, this situation will continue. We think the multiview learning research will be active for a long time, and further development and in-depth studies are needed to make it more effective and practical.
Contents:
1. Introduction
2. Multiview Semi-supervised Learning
3. Multiview Subspace Learning
4. Multiview Supervised Learning
5. Multiview Clustering
6. Multiview Active Learning
7. Multiview Transfer Learning and Multitask Learning
8. Multiview Deep Learning
9. View Construction
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