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This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field.
About the Author:
Moamar Sayed-Mouchaweh received his PhD from the University of Reims-France. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research centre in Sciences and Technology of the Information and the Communication. In December 2008, he obtained the Habilitation to Direct Research (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines Telecom Lille Douai (France), Department of Computer Science and Automatic Control. He edited and wrote several Springer books and served as a guest editor of several special issues of international journals. He also served as IPC Chair and conference Chair of several international workshops and conferences. He is serving as a member of the Editorial Board of several international Journals.
Title: Learning From Data Streams In Evolving
Publisher: Springer
Publication Date: 2018
Binding: Hardcover
Condition: New