Overview
- Combines information theory and statistical learning components in one volume
- Many chapters are contributed by authors that pioneered the presented methods themselves
- Interdisciplinary approach makes this book accessible to researchers and professionals in many areas of study
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Table of contents (16 chapters)
Keywords
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
Editors and Affiliations
Bibliographic Information
Book Title: Information Theory and Statistical Learning
Editors: Frank Emmert-Streib, Matthias Dehmer
DOI: https://doi.org/10.1007/978-0-387-84816-7
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2009
Hardcover ISBN: 978-0-387-84815-0Published: 14 November 2008
Softcover ISBN: 978-1-4419-4650-8Published: 04 November 2010
eBook ISBN: 978-0-387-84816-7Published: 24 November 2008
Edition Number: 1
Number of Pages: X, 439
Topics: Coding and Information Theory, Artificial Intelligence, Theory of Computation, Mathematics of Computing, Communications Engineering, Networks, Control, Robotics, Mechatronics