Overview
- Shows the state-of-the-art in dynamic learning, discussing advanced aspects and concepts
- Presenting open problems and future challenges in this field
- Examines the links between the different methods and techniques of dynamic learning in non-stationary environments
- Discusses multiple real-world problems in various application domains
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Table of contents (15 chapters)
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Front Matter
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Dynamic Methods for Unsupervised Learning Problems
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Front Matter
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Dynamic Methods for Supervised Classification Problems
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Front Matter
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Dynamic Methods for Supervised Classi?cation Problems
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Dynamic Methods for Supervised Regression Problems
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Front Matter
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Applications of Learning in Non-Stationary Environments
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Front Matter
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Applications of Learning in Non-stationary Environments
Keywords
About this book
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.
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Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy.
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Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations.
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This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
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Editors and Affiliations
Bibliographic Information
Book Title: Learning in Non-Stationary Environments
Book Subtitle: Methods and Applications
Editors: Moamar Sayed-Mouchaweh, Edwin Lughofer
DOI: https://doi.org/10.1007/978-1-4419-8020-5
Publisher: Springer New York, NY
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Science+Business Media New York 2012
Hardcover ISBN: 978-1-4419-8019-9
Softcover ISBN: 978-1-4899-9340-3
eBook ISBN: 978-1-4419-8020-5
Edition Number: 1
Number of Pages: XII, 440
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Pattern Recognition