Multiview Machine Learning

Shiliang Sun

ISBN 10: 981133028X / ISBN 13: 9789811330285

Edité par Springer Nature Singapore, Springer Nature Singapore, 2019

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9789811330285

Détails bibliographiques

• Titre: Multiview Machine Learning
• Éditeur: Springer Nature Singapore, Springer Nature Singapore
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 981133028X
• ISBN à 13 chiffres: 9789811330285
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Shiliang Sun received his Ph.D. degree in pattern recognition and intelligent systems from Tsinghua University, Beijing, China, in 2007. He is now a professor at the Department of Computer Science and Technology and the head of the Pattern Recognition and Machine Learning Research Group, East China Normal University, Shanghai, China. His current research interests include multiview learning, kernel methods, learning theory, probabilistic models, approximate inference, and sequential modeling. He has published 150+ research articles at peer-reviewed journals and international conferences. Prof. Sun is on the editorial board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, and Pattern Recognition.

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.