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Learning from Data Streams in Evolving Environments

Methods and Applications

Moamar Sayed-Mouchaweh (Hrsg.)

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ca. 96,29
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Springer International Publishing img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik

Beschreibung

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.

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Schlagwörter

quality control, reliability, safety and risk, Concept drift and concept evolution in data streams, Data streams in non-stationary environments, Machine Learning, Neural Networks and Learning Systems, Artificial Intelligence