2009

Information Theory and Statistical Learning

Editors:

ISBN: 978-0-387-84815-0 (Print) 978-0-387-84816-7 (Online)

Table of contents (16 chapters)

  1. Front Matter

    Pages i-x

  2. No Access

    Book Chapter

    Pages 1-23

    Algorithmic Probability: Theory and Applications

  3. No Access

    Book Chapter

    Pages 25-43

    Model Selection and Testing by the MDL Principle

  4. No Access

    Book Chapter

    Pages 45-82

    Normalized Information Distance

  5. No Access

    Book Chapter

    Pages 83-100

    The Application of Data Compression-Based Distances to Biological Sequences

  6. No Access

    Book Chapter

    Pages 101-123

    MIC: Mutual Information Based Hierarchical Clustering

  7. No Access

    Book Chapter

    Pages 125-152

    A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information

  8. No Access

    Book Chapter

    Pages 153-182

    Information Approach to Blind Source Separation and Deconvolution

  9. No Access

    Book Chapter

    Pages 183-207

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

  10. No Access

    Book Chapter

    Pages 209-230

    Information Theoretic Learning and Kernel Methods

  11. No Access

    Book Chapter

    Pages 231-265

    Information-Theoretic Causal Power

  12. No Access

    Book Chapter

    Pages 267-287

    Information Flows in Complex Networks

  13. No Access

    Book Chapter

    Pages 289-308

    Models of Information Processing in the Sensorimotor Loop

  14. No Access

    Book Chapter

    Pages 309-332

    Information Divergence Geometry and the Application to Statistical Machine Learning

  15. No Access

    Book Chapter

    Pages 333-354

    Model Selection and Information Criterion

  16. No Access

    Book Chapter

    Pages 355-384

    Extreme Physical Information as a Principle of Universal Stability

  17. No Access

    Book Chapter

    Pages 385-434

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

  18. Back Matter

    Pages 435-439