Multiview Machine Learning [electronic resource] /
Contributor(s): Mao, Liang [author.] | Dong, Ziang [author.] | Wu, Lidan [author.] | SpringerLink (Online service).Material type: BookPublisher: Singapore : Springer Singapore : Imprint: Springer, 2019Description: X, 149 p. 10 illus., 7 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811330292.Subject(s): Computer Science | Artificial intelligence | Optical pattern recognition | Computer vision | Data mining | Big data | Artificial Intelligence | Pattern Recognition | Image Processing and Computer Vision | Data Mining and Knowledge Discovery | Big DataOnline resources: Click here to access online
Chapter 1. Introduction -- Chapter 2. Multiview Semi-supervised Learning -- Chapter 3. Multiview Subspace Learning -- Chapter 4. Multiview Supervised Learning -- Chapter 5. Multiview Clustering -- Chapter 6. Multiview Active Learning -- Chapter 7. Multiview Transfer Learning and Multitask Learning -- Chapter 8. Multiview Deep Learning -- Chapter 9. View Construction.
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.