Information theory and statistical learning / [editors] Frank Emmert-Streib, Matthias Dehmer.

Format:

Book

Contributor:

Emmert-Streib, Frank.

Dehmer, Matthias, 1968-

Language:

English

Subjects (All):

Information theory.

Physical Description:

x, 439 pages : illustrations ; 24 cm

Place of Publication:

New York ; [London] : Springer, [2009]

Summary:

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.

Contents:

1 Algorithmic Probability: Theory and Applications / Ray J. Solomonoff 1

2 Model Selection and Testing by the MDL Principle / Jorma Rissanen 25

3 Normalized Information Distance / Paul M.B. Vitanyi, Frank J. Balbach, Rudi L. Cilibrasi, Ming Li 45

4 The Application of Data Compression-Based Distances to Biological Sequences / Attila Kertesz-Farkas, Andras Kocsor, Sandor Pongor 83

5 MIC: Mutual Information Based Hierarchical Clustering / Alexander Kraskov, Peter Grassberger 101

6 A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information / Jinjie Huang, Panxiang Rong 125

7 Information Approach to Blind Source Separation and Deconvolution / Pham Dinh-Tuan 153

8 Causality in Time Series: Its Detection and Quantification by Means of Information Theory / Katerina Hlavackova-Schindler 183

9 Information Theoretic Learning and Kernel Methods / Robert Jenssen 209

10 Information-Theoretic Causal Power / Kevin B. Korb, Lucas R. Hope, Erik P. Nyberg 231

11 Information Flows in Complex Networks / Joao Barros 267

12 Models of Information Processing in the Sensorimotor Loop / Daniel Polani, Marco Moller 289

13 Information Divergence Geometry and the Application to Statistical Machine Learning / Shinto Eguchi 309

14 Model Selection and Information Criterion / Noboru Murata, Hyeyoung Park 333

15 Extreme Physical Information as a Principle of Universal Stability / B. Roy Frieden 355

16 Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler / Reuven Rubinstein 385.

Notes:

Includes bibliographical references and index.

ISBN:

9780387848150

0387848150

0387848169

9780387848167

OCLC:

295000238