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Information Theory and Statistical Learning

Frank Emmert-Streib, Matthias Dehmer
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      "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
      Format: CONTRIBUTORS: Frank Emmert-Streib, Matthias Dehmer EAN: 9780387848150 COUNTRY: United States PAGES: WEIGHT: 1780 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2008-11-14 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Data Science / General, COMPUTERS / Information Theory, TECHNOLOGY & ENGINEERING / Telecommunications WIDTH: 155 cm SPINE:

      Book Themes:

      Information theory, Coding theory and cryptology, Automatic control engineering, Communications engineering / telecommunications, Mathematical theory of computation, Maths for computer scientists, Artificial intelligence

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      "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
      Format: CONTRIBUTORS: Frank Emmert-Streib, Matthias Dehmer EAN: 9780387848150 COUNTRY: United States PAGES: WEIGHT: 1780 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2008-11-14 CITY: GENRE: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Data Science / General, COMPUTERS / Information Theory, TECHNOLOGY & ENGINEERING / Telecommunications WIDTH: 155 cm SPINE:

      Book Themes:

      Information theory, Coding theory and cryptology, Automatic control engineering, Communications engineering / telecommunications, Mathematical theory of computation, Maths for computer scientists, Artificial intelligence

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