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Learning in Non-Stationary Environments

Methods and Applications

Editors: Sayed-Mouchaweh, Moamar, Lughofer, Edwin (Eds.)

  • Shows the state-of-the-art in dynamic learning
  • Details advanced aspects and concepts
  • Presents open problems and future challenges in the 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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eBook $159.00
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  • ISBN 978-1-4419-8020-5
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Hardcover $209.00
price for USA
  • ISBN 978-1-4419-8019-9
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  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
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  • ISBN 978-1-4899-9340-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
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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.

 

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.

 

Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations.

 

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.

 

Table of contents (15 chapters)

  • Prologue

    Sayed-Mouchaweh, Moamar (et al.)

    Pages 1-17

  • Incremental Statistical Measures

    Tschumitschew, Katharina (et al.)

    Pages 21-55

  • A Granular Description of Data: A Study in Evolvable Systems

    Pedrycz, Witold (et al.)

    Pages 57-75

  • Incremental Spectral Clustering

    Bouchachia, Abdelhamid (et al.)

    Pages 77-99

  • Semisupervised Dynamic Fuzzy K-Nearest Neighbors

    Hartert, Laurent (et al.)

    Pages 103-124

Buy this book

eBook $159.00
price for USA (gross)
  • ISBN 978-1-4419-8020-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $209.00
price for USA
  • ISBN 978-1-4419-8019-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
price for USA
  • ISBN 978-1-4899-9340-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Learning in Non-Stationary Environments
Book Subtitle
Methods and Applications
Editors
  • Moamar Sayed-Mouchaweh
  • Edwin Lughofer
Copyright
2012
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4419-8020-5
DOI
10.1007/978-1-4419-8020-5
Hardcover ISBN
978-1-4419-8019-9
Softcover ISBN
978-1-4899-9340-3
Edition Number
1
Number of Pages
XII, 440
Topics