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分类器的训练与学习是模式识别的一个重要环节,其目的在于按照某种算法,确定判决规则,使之具有自动分类识别的能力。本文介绍了采用Parzen窗法的随机模式分类器,并matlab实现了一个简易的随机模式分类器。-Classifier training and learning is an important part of pattern recognition, in accordance with the purpose of some kind of algorithm to determine
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两类二维相关正态分布条件下的最小错误率贝叶斯分类器，基于最小风险的贝叶斯分类器，Parzen窗法非参数估计分类器程序，Fisher线性判别法分类器程序。-Under normal conditions two types of two-dimensional correlation of minimum error rate of Bayesian classifier, the minimum risk-based Bayesian classifier, Parzen window meth
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本实验的目的是了解非参数估计--parzen窗估计，对数据进行Parzen窗估计和设计分类器。并分析窗口宽度h对分类的影响。-The purpose of this study is to understand the non-parametric estimation- parzen window estimation, Parzen window estimate of the data and design of classifiers. Window width h and analyz
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基于parzen窗口的贝叶斯分类，先对数据进行训练，再对测试数据进行识别。-Bayesian classification based on parzen window of data, training, and identification of the test data.
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模式识别在MATLAB程序下的有关算法程序，包括Fisher分类，ML分类，parzen窗分类，直方图画法，roc曲线，roc分类等(Pattern recognition in the MATLAB program related algorithm program, including Fisher classification, ML classification, Parzen window classification, histogram drawing, ROC curve, RO
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