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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.
| Serija: | Studies in Big Data |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 328 |
| ISBN-10: | 3319898027 |
| ISBN-13: | 9783319898025 |
| Formatas: | 241 x 160 x 24 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Learning from Data Streams in Evolving Environments: Methods and Applications“
