Information Theory and Statistical Learning

  • Frank Emmert-Streib
  • Matthias Dehmer

Table of contents

  1. Front Matter
    Pages i-x
  2. Paul M. B. Vitányi, Frank J. Balbach, Rudi L. Cilibrasi, Ming Li
    Pages 45-82
  3. Attila Kertesz-Farkas, Andras Kocsor, Sandor Pongor
    Pages 83-100
  4. Alexander Kraskov, Peter Grassberger
    Pages 101-123
  5. Kevin B. Korb, Lucas R. Hope, Erik P. Nyberg
    Pages 231-265
  6. João Barros
    Pages 267-287
  7. Daniel Polani, Marco Möller
    Pages 289-308
  8. Noboru Murata, Hyeyoung Park
    Pages 333-354
  9. Back Matter
    Pages 435-439

About this book

Introduction

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

Keywords

algorithms combinatorial optimization data compression information information theory kernel method machine learning optimization stability

Editors and affiliations

  • Frank Emmert-Streib
    • 1
    • 2
  • Matthias Dehmer
    • 3
    • 4
  1. 1.Department of Biostatistics and Department of Genome SciencesUniversity of WashingtonSeattleUSA
  2. 2.Queen's University Belfast Computational Biology and Machine LearningCenter for Cancer Research and Cell Biology School of Biomedical SciencesBelfastUK
  3. 3.Institute of Discrete Mathematics and GeometryVienna University of TechnologyViennaAustria
  4. 4.Probability and StatisticsUniversity of Coimbra Center for MathematicsCoimbraPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-84816-7
  • Copyright Information Springer US 2009
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-84815-0
  • Online ISBN 978-0-387-84816-7
  • About this book