Michael R. Lyu – författare

Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.

Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering departmentof the Chinese University of Hong Kong.

Quality-of-Service (QoS) is normally used to describe the non-functional characteristics of Web services and as a criterion for evaluating different Web services. QoS Management of Web Services presents a new distributed QoS evaluation framework for these services. Moreover, three QoS prediction methods and two methods for creating fault-tolerant Web services are also proposed in this book. It not only provides the latest research results, but also presents an excellent overview of QoS management of Web sciences, making it a valuable resource for researchers and graduate students in service computing.

Zibin Zheng is an associate research fellow at the Shenzhen Research Institute, The Chinese University of Hong Kong, China. Professor Michael R. Lyu also works at the same institute.

This book offers a systematic and practical overview of Quality of Service prediction in cloud and service computing. Intended to thoroughly prepare the reader for research in cloud performance, the book first identifies common problems in QoS prediction and proposes three QoS prediction models to address them. Then it demonstrates the benefits of QoS prediction in two QoS-aware research areas. Lastly, it collects large-scale real-world temporal QoS data and publicly releases the datasets, making it a valuable resource for the research community. The book will appeal to professionals involved in cloud computing and graduate students working on QoS-related problems.