Learning from Data Streams in Evolving Environments

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.
  

Learning from Data Streams in Evolving Environments features in the following genres: Communications engineering / telecommunications, Security and fire alarm systems, Automatic control engineering, Expert systems / knowledge-based systems, Data mining

Learning from Data Streams in Evolving Environments is available in Paperback, Hardback

Learning from Data Streams in Evolving Environments was written by Moamar SayedMouchaweh and published by Springer an imprint of Springer International Publishing

Learning from Data Streams in Evolving Environments has 317 pages

Yes it is part of Studies in Big Data series