Multiview Machine Learning eBook

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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.

This is a digital product.

Multiview Machine Learning is written by Shiliang Sun; Liang Mao; Ziang Dong; Lidan Wu and published by Springer. The Digital and eTextbook ISBNs for Multiview Machine Learning are 9789811330292, 9811330298 and the print ISBNs are 9789811330285, 981133028X.