Information Theory and Statistical Learning

Matthias Dehmer

ISBN 10: 1441946500 / ISBN 13: 9781441946508

Editore: Springer US, Springer US, 2010

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Druck auf Anfrage Neuware - Printed after ordering - '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.

Codice articolo 9781441946508

Dati bibliografici

• Titolo: Information Theory and Statistical Learning
• Editore: Springer US, Springer US
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441946500
• ISBN 13: 9781441946508
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 25 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 25 millimetri di profondità
• Peso: 680 grammi

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Riassunto

"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