| 书目名称 | Information Theory and Statistical Learning | | 编辑 | Frank Emmert-Streib,Matthias Dehmer | | 视频video | | | 概述 | Combines information theory and statistical learning components in one volume.Many chapters are contributed by authors that pioneered the presented methods themselves.Interdisciplinary approach makes | | 图书封面 |  | | 描述 | ."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. | | 出版日期 | Book 2009 | | 关键词 | algorithms; combinatorial optimization; data compression; information; information theory; kernel method; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-0-387-84816-7 | | isbn_softcover | 978-1-4419-4650-8 | | isbn_ebook | 978-0-387-84816-7 | | copyright | Springer-Verlag US 2009 |
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发表于 2025-3-21 18:42:54
