Information Theory and Statistical Learning (Paperback)

Frank Emmert-Streib

ISBN 10: 1441946500 / ISBN 13: 9781441946508

Published by Springer-Verlag New York Inc., New York, NY, 2010

About this Item

Paperback. "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 "Information Theory and Statistical Learning" presents theoretical and practical results about information theoretic methods used in the context of statistical learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781441946508

Bibliographic Details

• Title: Information Theory and Statistical Learning (Paperback)
• Publisher: Springer-Verlag New York Inc., New York, NY
• Publication Date: 2010
• Language: English
• ISBN 10: 1441946500
• ISBN 13: 9781441946508
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

"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