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分类器程序，混合高斯分类器，用于语音图像的分类处理-classifier procedures, Gaussian mixture classifier for the classification of voice processing images
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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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现有数字信号自动调制识别方法大多只适用于无记忆信号，如PSK、ASK、FSK信号等。将有记忆
信号(MSK信号)和无记忆信号一起考虑，提出了一种改进的数字信号自动识别方法。该方法采用信号的瞬时统
计量作为特征参数，采用多层神经网络作为分类器。计算机仿真表明：当噪声采用高斯白噪声，并且信噪比大于
l5 dB时，识别率高于96％ ；当信噪比不低于l0 dB时，识别率不低于90％。,Existing digital signal automatic modulation recognition
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The High Dimensional Discriminant Analysis (HDDA) toolbox contains an efficient supervised classifier for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard
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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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图像模式识别中使用的高斯型贝叶斯分类器，提供完整的参数选择-Gaussian Bayesian classifier
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The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.
Reference: C. Bouveyron, S. Girard and
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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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《数字图像处理》源码
部分函数列表
bayesgauss - Bayes classifier for Gaussian patterns.
bound2eight - Convert 4-connected boundary to 8-connected.
bound2four - Convert 8-connected boundary to 4-connected.
bound2im - Convert boundary to image.
boundar
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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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该文提出一种基于广义分数阶傅里叶变换和分数低阶Wigner-Ville 分布的数字调制识别新方法，该方法提取广义分数阶傅里叶变换的零
中心归一化瞬时幅度谱密度的最大值和分数低阶Wigner-Ville 分布幅度的最大值作为识别特征参数，并采用判决树分类器，实现了非高斯噪声下数字调制信号识别。-This paper presents a generalized fractional Fourier transform and fractional lower order Wigner-Vill
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Gaussian support vector binary classifier
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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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简单的分类小程序。包括高斯混合模型、svm（调用函数）、logistic regression、和人工神经网络-Simple classification applet. Including the Gaussian mixture model, svm (calling function), logistic regression, and artificial neural networks
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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 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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