Multiview Machine Learning (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	2019
First published:	2019
Authors:	Shiliang Sun • Liang Mao • Ziang Dong • Lidan Wu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	149
Edition:	1st ed. 2019
ISBN-13:	978-981-13-3028-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Image processing > General
LSN:	981-13-3028-X
Barcode:	9789811330285

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