Information Theory and Statistical Learning (eBook, PDF)

• Format: PDF

Produktbeschreibung

"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

Produktdetails

• Verlag: Springer US
• Erscheinungstermin: 24. November 2008
• Englisch
• ISBN-13: 9780387848167
• Artikelnr.: 37286890

Inhaltsangabe

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.

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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.