Learning from Data Streams in Evolving Environments

Moamar Sayed-Mouchaweh

ISBN 10: 3030078620 / ISBN 13: 9783030078621

Published by Springer International Publishing, Springer International Publishing Dez 2018, 2018

About this Item

Neuware -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.Provides multiple examples to facilitate the understanding data streams in non-stationary environments;Presents several application cases to show how the methods solve different real world problems;Discusses the links between methods to help stimulate new research and application directions.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.

Seller Inventory # 9783030078621

Bibliographic Details

• Title: Learning from Data Streams in Evolving Environments
• Publisher: Springer International Publishing, Springer International Publishing Dez 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3030078620
• ISBN 13: 9783030078621
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

• Provides multiple examples to facilitate the understanding data streams in non-stationary environments;
  
• Presents several application cases to show how the methods solve different real world problems;
  
• Discusses the links between methods to help stimulate new research and application directions.
  

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.