Information Theory and Statistical Learning

"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

• algorithms
• combinatorial optimization
• data compression
• information
• information theory
• kernel method
• machine learning
• optimization
• stability

• 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

Policies and ethics