Pattern Recognition Principles
Pattern Recognition Principles
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DOI：
10.1201/9781420090741.ch2
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发表时间：
1974
期刊：
影响因子：
--
通讯作者：
Julius T. Tou;Rafael Gonzalez
中科院分区：
文献类型：
--
作者：
Julius T. Tou;Rafael Gonzalez
本文介绍了模式处理与识别系统的分析与设计的基本原理和现有技术。所涵盖的领域包括决策功能，模式分类的距离函数，模式分类的似然函数，感知器和潜在的功能方法，可训练的模式分类器，统计方法，可训练的分类器，模式预处理和特征选择，和句法模式识别。
The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems. Areas covered include decision functions, pattern classification by distance functions, pattern classification by likelihood functions, the perceptron and the potential function approaches to trainable pattern classifiers, statistical approach to trainable classifiers, pattern preprocessing and feature selection, and syntactic pattern recognition.