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Information Theory and Statistical Learning

  • Book
  • © 2009

Overview

Editors:
  1. Frank Emmert-Streib

    1. Department of Biostatistics and Department of Genome Sciences, University of Washington, Seattle, USA
      Queen's University Belfast Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology School of Biomedical Sciences, Belfast, UK

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  2. Matthias Dehmer

    1. Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Vienna, Austria
      Probability and Statistics, University of Coimbra Center for Mathematics, Coimbra, Portugal

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    You can also search for this editor in PubMed   Google Scholar

  • 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)

  1. Front Matter

    Pages i-x
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  2. Algorithmic Probability: Theory and Applications

    • Ray J. Solomonoff
    Pages 1-23

  3. Model Selection and Testing by the MDL Principle

    • Jorma Rissanen
    Pages 25-43

  4. Normalized Information Distance

    • Paul M. B. Vitányi, Frank J. Balbach, Rudi L. Cilibrasi, Ming Li
    Pages 45-82

  5. The Application of Data Compression-Based Distances to Biological Sequences

    • Attila Kertesz-Farkas, Andras Kocsor, Sandor Pongor
    Pages 83-100

  6. MIC: Mutual Information Based Hierarchical Clustering

    • Alexander Kraskov, Peter Grassberger
    Pages 101-123

  7. A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

    • Jinjie Huang, Panxiang Rong
    Pages 125-152

  8. Information Approach to Blind Source Separation and Deconvolution

    • Pham Dinh-Tuan
    Pages 153-182

  9. Causality in Time Series: Its Detection and Quantification by Means of Information Theory

    • Kateřina Hlaváčková-Schindler
    Pages 183-207

  10. Information Theoretic Learning and Kernel Methods

    • Robert Jenssen
    Pages 209-230

  11. Information-Theoretic Causal Power

    • Kevin B. Korb, Lucas R. Hope, Erik P. Nyberg
    Pages 231-265

  12. Information Flows in Complex Networks

    • João Barros
    Pages 267-287

  13. Models of Information Processing in the Sensorimotor Loop

    • Daniel Polani, Marco Möller
    Pages 289-308

  14. Information Divergence Geometry and the Application to Statistical Machine Learning

    • Shinto Eguchi
    Pages 309-332

  15. Model Selection and Information Criterion

    • Noboru Murata, Hyeyoung Park
    Pages 333-354

  16. Extreme Physical Information as a Principle of Universal Stability

    • B. Roy Frieden
    Pages 355-384

  17. Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler

    • Reuven Rubinstein
    Pages 385-434

  18. Back Matter

    Pages 435-439
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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

  • Department of Biostatistics and Department of Genome Sciences, University of Washington, Seattle, USA

    Frank Emmert-Streib

  • Queen's University Belfast Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology School of Biomedical Sciences, Belfast, UK

    Frank Emmert-Streib

  • Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Vienna, Austria

    Matthias Dehmer

  • Probability and Statistics, University of Coimbra Center for Mathematics, Coimbra, Portugal

    Matthias Dehmer

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