Learning from data streams in evolving environments : methods and applications / Moamar Sayed-Mouchaweh, editor.
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 interpr...
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| Other Authors: | |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2019]
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| Series: | Studies in big data ;
v. 41. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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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| Physical Description: | 1 online resource. |
| ISBN: | 3319898027 9783319898025 3319898035 9783319898032 |