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Information Theory and Statistical Learning

Editors: Emmert-Streib, Frank, Dehmer, Matthias (Eds.)

  • Combines information theory and statistical learning components in one volume
  • Many chapters are contributed by authors who pioneered the presented methods themselves
  • Interdisciplinary approach makes this book accessible to researchers and professionals in many areas of study
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eBook $109.00
price for USA
  • ISBN 978-0-387-84816-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $149.00
price for USA
  • ISBN 978-0-387-84815-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $149.00
price for USA
  • ISBN 978-1-4419-4650-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning.

The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines.

Advance Praise for Information Theory and Statistical Learning:

"A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places."

-- Shun-ichi Amari, RIKEN Brain Science Institute,  Professor-Emeritus at the University of Tokyo

Table of contents (16 chapters)

  • Algorithmic Probability: Theory and Applications

    Solomonoff, Ray J.

    Pages 1-23

  • Model Selection and Testing by the MDL Principle

    Rissanen, Jorma

    Pages 25-43

  • Normalized Information Distance

    Vitányi, Paul M. B. (et al.)

    Pages 45-82

  • The Application of Data Compression-Based Distances to Biological Sequences

    Kertesz-Farkas, Attila (et al.)

    Pages 83-100

  • MIC: Mutual Information Based Hierarchical Clustering

    Kraskov, Alexander (et al.)

    Pages 101-123

Buy this book

eBook $109.00
price for USA
  • ISBN 978-0-387-84816-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $149.00
price for USA
  • ISBN 978-0-387-84815-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $149.00
price for USA
  • ISBN 978-1-4419-4650-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Information Theory and Statistical Learning
Editors
  • Frank Emmert-Streib
  • Matthias Dehmer
Copyright
2009
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-84816-7
DOI
10.1007/978-0-387-84816-7
Hardcover ISBN
978-0-387-84815-0
Softcover ISBN
978-1-4419-4650-8
Edition Number
1
Number of Pages
X, 439
Topics