Introduction
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.
Keywords
Machine LearningNeural Networks and Learning SystemsArtificial IntelligenceData streams in non-stationary environmentsConcept drift and concept evolution in data streams