Multiview Machine Learning Shiliang Sun Liang Mao Ziang Dong by Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu 9789811330285, 981133028X instant download after payment.

During the past two decades, multiview learning as an emerging direction in
machine learning became a prevailing research topic in artificial intelligence (AI).
Its success and popularity were largely motivated by the fact that real-world
applications generate various data as different views while people try to manipulate
and integrate those data for performance improvements. In the data era, this situation
will continue. We think the multiview learning research will be active for a
long time, and further development and in-depth studies are needed to make it more
effective and practical.
In 2013, a review paper of mine, entitled “A Survey of Multi-view Machine
Learning” (Neural Computing and Applications, 2013), was published. It generates
a good dissemination and promotion of multiview learning and has been well cited.
Since then, much more research has been developed. This book aims to provide an
in-depth and comprehensive introduction to multiview learning and hope to be
helpful for AI researchers and practitioners.
I have been working in the machine learning area for more than 15 years. Most
of my work introduced in this book was completed after I graduated from Tsinghua
University and joined East China Normal University in 2007. And we also include
many important and representative works from other researchers to make the book
content complete and comprehensive. Due to space and time limits, we may not be
able to include all relevant works.
I owe many thanks to the past and current members of my Pattern Recognition
and Machine Learning Research Group, East China Normal University, for their
hard work to make research done in time. The relationship between me and them is
not just professors and students, but also comrades-in-arms.