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bayes分类器 Bayes classifier for Gaussian distributiuon 直接可以解压缩-Bayes classifier Bayesian classifier for Gaussian di stributiuon can directly extract
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贝叶斯分类器，模式识别，贝叶斯规则用于C个类，高斯函数建模-Bayesian classifier, pattern recognition, Bayesian rules for C-class, the Gaussian function modeling
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一个强大的统计模式识别工具箱，包含高斯分类器，高斯混合模型，主成分分析，支持向量机等常见分类方法。-A powerful statistical pattern recognition toolbox, including the Gaussian classifier, Gaussian mixture model, principal component analysis, support vector machines and other common classification met
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本贝叶斯分类器可以实现对二维高斯分布样本的分类-The Bayesian classifier can achieve two-dimensional Gaussian distribution of the classification of samples
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上传-混合高斯贝叶斯网络分类器，有数据可以直接运行，互相交流，互相学习。-Upload- Gaussian mixture Bayesian network classifier, the data can be directly run, exchange, learn from each other.
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模式识别某次课程的作业，完成了高斯分布下的两种贝叶斯分类器,以及非参数的K近邻、Parzen窗方法，采用UCI机器学习数据库中的某些数据作为样本，使用交叉验证方法确定参数-Pattern recognition of a particular course work, completed under the two Gaussian Bayesian classifier, and the non-parametric K-nearest neighbor, Parzen window meth
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贝叶斯分类器，首先生成3000个高斯分布的点，1000个点做训练集，2000个点做测试集。先运行data_generator.m自动生成两个集盒，再运行bayes_classifier.m进行分类-Bayesian classifier, the first generation 3000 Gaussian distribution of points, 1000 points to do the training set, 2000 points to do the test set. Aut
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模式识别 二维高斯随机数 fisher线性分类器-Two-dimensional Gaussian random number pattern recognition fisher linear classifier
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2类分类高斯模型
每个类是由一个单一的多元高斯分布的3-D建模
显示如何估计高斯均值向量和协方差矩阵的最大似然（ML）估计的基础上为每个类。
meanA和meanB代表每个类的均值，varA和varB的的代表每个类的协方差矩阵.-2-class classifier with Gaussian Models
Each class is modelled by a single 3-D multivariate Gaussian distribution
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程序展示了bayes classifier对于连续数据的应用，假设数据均服从高斯分布。程序包含了binary classification和multi-classification的例子。-Program shows bayes classifier for continuous data applications, assume that the data are Gaussian. Program includes a binary classification and multi-clas
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Bayes classifier
Step 1. : Generate an arbitrary 3-class dataset with bi-variate Gaussian distribution.
Step 2. : If three arbitrary samples are given as follows, determine to which class (as each class is generated by Step 1) each sample s
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Gaussian classifier and some important homework material usual machine learning pattern recognition courses-Gaussian classifier and some important homework material usual machine learning pattern recognition courses
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一个小demo,用softmax实现数据分类，数据有4类，均服从高斯分布-A small demo, a classifier with softmax softmax is implemented to classify 4 classes of datas which are generated by gaussian distribution
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一个小demo,用softmax实现数据分类，数据有4类，均服从高斯分布-A little demo, a classifier with softmax softmax is implemented to classify 4 classes of datas which are generated by gaussian distribution
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