| 书目名称 | Information Geometry and Its Applications | | 编辑 | Shun-ichi Amari | | 视频video | | | 概述 | Introduces information geometry intuitively to readers without knowledge of differential geometry.Includes hot topics of applications to machine learning, signal processing, neural networks, and optim | | 丛书名称 | Applied Mathematical Sciences | | 图书封面 |  | | 描述 | This is the first comprehensive book on information geometry, written by the founder of the field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide range of applications, covering information science, engineering, and neuroscience. It consists of four parts, which on the whole can be read independently. A manifold with a divergence function is first introduced, leading directly to dualistic structure, the heart of information geometry. This part (Part I) can be apprehended without any knowledge of differential geometry. An intuitive explanation of modern differential geometry then follows in Part II, although the book is for the most part understandable without modern differential geometry. Information geometry of statistical inference, including time series analysis and semiparametric estimation (the Neyman–Scott problem), is demonstrated concisely in Part III. Applications addressed in Part IV include hot current topics in machine learning,signal processing, optimization, and neural networks. The book is interdisciplinary, connecting mathematics, information sciences, physics, and neurosciences, inviting readers to a new world of information | | 出版日期 | Book 2016 | | 关键词 | Dual differential geometry; Information geometry; Machine learning; Mathematical neuroscience; Natural g | | 版次 | 1 | | doi | https://doi.org/10.1007/978-4-431-55978-8 | | isbn_softcover | 978-4-431-56743-1 | | isbn_ebook | 978-4-431-55978-8Series ISSN 0066-5452 Series E-ISSN 2196-968X | | issn_series | 0066-5452 | | copyright | Springer Japan 2016 |
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发表于 2025-3-21 18:25:04
