| 书目名称 | Robustness in Statistical Pattern Recognition | | 编辑 | Yurij Kharin | | 视频video | | | 丛书名称 | Mathematics and Its Applications | | 图书封面 |  | | 描述 | This book is concerned with important problems of robust (stable) statistical pat tern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which prin ciples and methods are constructed for classification and identification of objects, phenomena, processes, situations, and signals, i. e. , of objects that can be specified by a finite set of features, or properties characterizing the objects (Mathematical Encyclopedia (1984)). Two stages in development of the mathematical theory of pattern recognition may be observed. At the first stage, until the middle of the 1970s, pattern recogni tion theory was replenished mainly from adjacent mathematical disciplines: mathe matical statistics, functional analysis, discrete mathematics, and information theory. This development stage is characterized by successful solution of pattern recognition problems of different physical nature, but of the simplest form in the sense of used mathematical models. One of the main approaches to solve pattern recognition problems is the statisti cal approach, which uses stochastic models of feature | | 出版日期 | Book 1996 | | 关键词 | classification; cluster analysis; cognition; control; cybernetics; mathematics; modeling; pattern recogniti | | 版次 | 1 | | doi | https://doi.org/10.1007/978-94-015-8630-6 | | isbn_softcover | 978-90-481-4760-1 | | isbn_ebook | 978-94-015-8630-6 | | copyright | Springer Science+Business Media Dordrecht 1996 |
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发表于 2025-3-21 18:44:52
