| 书目名称 | Statistical and Neural Classifiers | | 副标题 | An Integrated Approa | | 编辑 | Šarūnas Raudys | | 视频video | | | 概述 | Covers the state of the art in this important area.Shows the reader how neural network classifiers actually work | | 丛书名称 | Advances in Computer Vision and Pattern Recognition | | 图书封面 |  | | 描述 | Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its recognition/classification may consist of one of the following two tasks: (1) supervised classification (also called discriminant analysis); the input pattern is assigned to one of several predefined classes, (2) unsupervised classification (also called clustering); no pattern classes are defined a priori and patterns are grouped into clusters based on their similarity. Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding (e. g. , bioinformatics, data mining, document classification, and multimedia database retrieval). Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learni | | 出版日期 | Book 2001 | | 关键词 | Excel; Image Processing; MATLAB; Maxima; Multimedia; Neural Networks; Pattern Recognition; Performance; Spee | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-0359-2 | | isbn_softcover | 978-1-4471-1071-2 | | isbn_ebook | 978-1-4471-0359-2Series ISSN 2191-6586 Series E-ISSN 2191-6594 | | issn_series | 2191-6586 | | copyright | Springer-Verlag London 2001 |
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发表于 2025-3-21 19:43:06
