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图像模式识别
模板匹配法
神经网络分类器
几何分类器
基于概率统计的贝叶斯分类器-image pattern recognition template matching neural network classifiers classification is based on geometric probability and statistics Bayesian classifier
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模式识别 作业 实现自动产生样本，并用最近距离法，贝叶斯分类，Parzen窗概率密度估计-Pattern recognition operations automatically generate the sample, and with the recent distance method, Bayesian classifier, Parzen window probability density estimation
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模式识别 贝叶斯决策理论 概率密度函数估计 线性判别函数 非线性判别函数 临近法-Bayesian decision theory, pattern recognition, probability density function estimated linear discriminant function method of nonlinear discriminant function near
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利用最小错分概率贝叶斯分类器进行图像分类的基本方法-Minimum probability of misclassification Bayesian classifier for image classification
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模式识别的内容，包括模式识别的基本概念、模式识别方法及应用。具体的内容包括：正则化网络、Bayes决策理论、分类器组合、统计学习理论、概率密度估计、非监督学习方法-Pattern recognition, including the basic concepts of pattern recognition, pattern recognition methods and applications.Specific content, including: Regularization Netwo
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用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-Using the supervision parameter estimation in Bayesian method estimates the parameters of the conditional probability density u
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实现贝叶斯分类器，按最大概率和最小风险的分类决策-Implement Bayesian classifier, according to the maximum and minimum risk probability of classification decisions
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