Learning from Data Streams in Evolving Environments
Methods and Applications
Nonfiction, Science & Nature, Technology, Quality Control, Telecommunications
| Author: | ISBN: | 9783319898032 | |
| Publisher: | Springer International Publishing | Publication: | July 28, 2018 |
| Imprint: | Springer | Language: | English |
| Author: | |
| ISBN: | 9783319898032 |
| Publisher: | Springer International Publishing |
| Publication: | July 28, 2018 |
| Imprint: | Springer |
| Language: | English |
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.
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Provides multiple examples to facilitate the understanding data streams in non-stationary environments;
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Presents several application cases to show how the methods solve different real world problems;
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Discusses the links between methods to help stimulate new research and application directions.
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.
