Description: 贝叶斯分类器的设计实验，内有解释利于入门学习-Bayesian classifier design experiments, which help to explain the study entry Platform: |
Size: 1024 |
Author:路单 |
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Description: 贝叶斯分类器源代码．能很好的实现分类．是很好的学习资料．期望与大家一起分享．-Bayesian classifier source code. Can achieve very good classification. Is a very good learning materials. Look forward to working with you to share Platform: |
Size: 2048 |
Author:huang fang |
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Description: 贝叶斯分类器实现多类识别，主要用于两类的识别-BAYES_CLASSIFIER function calculates the discriminant functions for
two classes. Platform: |
Size: 1024 |
Author:xingtao |
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Description: 希腊希尔维亚的模式识别的源码。可以用来学习模式识别。-Source code in Matlab for Pattern Recogintion from xi la Platform: |
Size: 15457280 |
Author:Baoqing Yang |
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Description: 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. Automatically generated the first two sets running data_generator.m box, and then run bayes_classifier.m classification Platform: |
Size: 2048 |
Author:飞兔 |
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Description: 在matlab环境下的一个朴素贝叶斯分类器，大家可以参考下-Matlab environment a Naive Bayes classifier, we can refer to the following Platform: |
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Author:zxk |
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Description: 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 should belong.-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 should belong. Platform: |
Size: 2048 |
Author:jyju |
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Description: z=bayes_classifier(m,S,P,X). This function outputs the Bayesian classification
rule for M classes, each modeled by a Gaussian distribution.
where,
M: the number of classes.
• l: the number of features (for each feature vector).
• N: the number of data vectors.
• m: lxM matrix, whose j-th column corresponds to the mean of the j-th class.
• S: lxlxM matrix. S(:,:,j) is the covariance matrix of the j-th normal distribution.
• P: M-dimensional vector whose j-th component is the a priori probability of the j-th class.
• X: lxM data matrix, whose rows are the feature vectors, i.e., data matrix in scikit-learn convention.
• y: N-dimensional vector containing the known class labels, i.e., the ground truth, or target
vector in scikit-learn convention.
• z: N-dimensional vector containing the predicted class labels, i.e., the vector of predicted class
labels in scikit-learn convention.-z=bayes_classifier(m,S,P,X). This function outputs the Bayesian classification
rule for M classes, each modeled by a Gaussian distribution.
where,
M: the number of classes.
• l: the number of features (for each feature vector).
• N: the number of data vectors.
• m: lxM matrix, whose j-th column corresponds to the mean of the j-th class.
• S: lxlxM matrix. S(:,:,j) is the covariance matrix of the j-th normal distribution.
• P: M-dimensional vector whose j-th component is the a priori probability of the j-th class.
• X: lxM data matrix, whose rows are the feature vectors, i.e., data matrix in scikit-learn convention.
• y: N-dimensional vector containing the known class labels, i.e., the ground truth, or target
vector in scikit-learn convention.
• z: N-dimensional vector containing the predicted class labels, i.e., the vector of predicted class
labels in scikit-learn convention. Platform: |
Size: 1024 |
Author:mnzars |
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Search - bayes_classifier