Multiview Machine Learning (PDF)
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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.
Liang Mao is a senior Ph.D. student at the Department of Computer Science and Technology and the Pattern Recognition and Machine Learning Research Group, East China Normal University, Shanghai, China. His main research interest is multiview learning and probabilistic models.
- Autoren: Shiliang Sun , Liang Mao , Ziang Dong , Lidan Wu
- 2019, 1st ed. 2019, 149 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 9811330298
- ISBN-13: 9789811330292
- Erscheinungsdatum: 07.01.2019
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 2.29 MB
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