TY  - BOOK
AU  - Emmert-Streib,Frank
AU  - Dehmer,Matthias
ED  - SpringerLink (Online service)
TI  - Information Theory and Statistical Learning
SN  - 9780387848167
U1  - 004.0151 23
PY  - 2009///
CY  - Boston, MA
PB  - Springer US
KW  - COMPUTER SCIENCE
KW  - INFORMATION THEORY
KW  - ARTIFICIAL INTELLIGENCE
KW  - STATISTICS
KW  - CONTROL ENGINEERING SYSTEMS
KW  - TELECOMMUNICATION
KW  - THEORY OF COMPUTATION
KW  - ARTIFICIAL INTELLIGENCE (INCL. ROBOTICS)
KW  - MATHEMATICS OF COMPUTING
KW  - COMMUNICATIONS ENGINEERING, NETWORKS
KW  - CONTROL , ROBOTICS, MECHATRONICS
KW  - STATISTICS, GENERAL
N1  - Algorithmic Probability: Theory and Applications -- Model Selection and Testing by the MDL Principle -- Normalized Information Distance -- The Application of Data Compression-Based Distances to Biological Sequences -- MIC: Mutual Information Based Hierarchical Clustering -- A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information -- Information Approach to Blind Source Separation and Deconvolution -- Causality in Time Series: Its Detection and Quantification by Means of Information Theory -- Information Theoretic Learning and Kernel Methods -- Information-Theoretic Causal Power -- Information Flows in Complex Networks -- Models of Information Processing in the Sensorimotor Loop -- Information Divergence Geometry and the Application to Statistical Machine Learning -- Model Selection and Information Criterion -- Extreme Physical Information as a Principle of Universal Stability -- Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler
N2  - 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
UR  - http://dx.doi.org/10.1007/978-0-387-84816-7
ER  -