Table of contents
About this book
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.
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-319-89803-2
- Copyright Information Springer International Publishing AG, part of Springer Nature 2019
- Publisher Name Springer, Cham
- eBook Packages Engineering
- Print ISBN 978-3-319-89802-5
- Online ISBN 978-3-319-89803-2
- Series Print ISSN 2197-6503
- Series Online ISSN 2197-6511
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