Description: Programs to induce a naive possibilistic classifier (a possibilistic analog of the naive Bayes classifier) and to classify new data with an induced naive possibilistic classifier.-Programs to induce a naive possibilistic c lassifier (a possibilistic analog of the naive Bayesian classifier) and to classify new data with an induced naive possibilistic classifier. Platform: |
Size: 43008 |
Author:无心 |
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Description: 模式识别分类程序，贝叶斯，神经网络分类训练程序，很不错啊-the classification procedures, Bayesian neural network classifier training procedures, and it is very responds : Platform: |
Size: 3755008 |
Author:杨宁 |
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Description: 一个由Mike Gashler完成的机器学习方面的includes neural net, naive bayesian classifier, decision tree, KNN, a genetic algorithm, and some manifold learning algorithms. -by Mike Gashler a complete machine learning includes the neur al net, naive bayesian classifier. decision tree, KNN, a genetic algorithm, manifold and some learning algorithms. Platform: |
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Author:lyb |
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Description: 基于文献《Boosted Bayesian Network Classifiers》的实现代码。-Based on the literature Boosted Bayesian Network Classifiers the realization of the code. Platform: |
Size: 64512 |
Author:minghongxie |
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Description: 帮师姐做的毕业论文：基于朴素贝叶斯的文本分类器，使用Visual C# 2005编写，文本语料库包括测试语料库和训练语料库（保存再Access数据库中）-师姐help make the thesis: the text-based Naive Bayesian classifier, the use of Visual C# 2005 to prepare, text corpora, including test and training corpus Corpus (preservation-Access database) Platform: |
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Author:贾洋洋 |
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Description: 贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training Platform: |
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Author:guifeng2002 |
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Description: 《机器学习》课上的作业，PCA和LDA降维，尽管网上很多，但很少注释，另外细节上也没注意。这里有很详细的注释。另外还附上一个Naive贝叶斯分类器，大家可以作比较。附带的图像包是OLR人脸。ReducedDim为想要提取的特征数，不是百分比！-" Machine learning" classes on the homework, PCA and LDA dimensionality reduction, even though a lot of online, but few notes, on the other did not pay attention to details. Here there is a very detailed notes. In addition, attach a Naive Bayesian classifier, we can compare. Fringe image packages are OLR people face. ReducedDim you want to extract the characteristics of a few, not a percentage! Platform: |
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Author: |
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Description: ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K—
me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记
录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据
集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类准确率。-: K-means algorithm will be introduced to the Naive Bayesian Classifier study, a K-means based on the Naive Bayesian classification algorithm. First of all, with K-me. arks algorithm focus on the raw data of the complete data subset of the cluster, the calculation of missing data for each subset of records and the similarity between the cluster center of gravity to the nearest record assigned to a cluster, and the corresponding attributes of the cluster means to fill the missing value record, and then use Naive Bayes classification algorithm to deal with the data set after classification. The experimental results show that compared with the Naive Bayes, K-means based on the thinking of Naive Bayes algorithm has higher classification accuracy. Platform: |
Size: 173056 |
Author:李浩 |
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Description: 一个基础贝叶斯变换的压缩感知，包含一个源代码和一个一维信号处理的例子和两个二维图像的例子-The basic BCS implemented via a variational Bayesian approach. The package includes the core VB-BCS code, one example of a 1-dimensional signal and two examples of 2-dimensional images. Platform: |
Size: 92160 |
Author:chen |
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Description: 贝叶斯分类和后面的线性、非线性分类器属于有监督学习。
-Bayesian classification and the back of the linear, non-linear classifier belong to supervised learning. Platform: |
Size: 485376 |
Author:hkm |
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Description: 用c和Java写的贝叶斯分类器，针对垃圾邮件处理，可用于过滤垃圾邮件。-With c and the Bayesian classifier written in Java for dealing with junk e-mail can be used to filter spam. Platform: |
Size: 631808 |
Author:Avenway |
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Description: 贝叶斯分类器的设计，其中包括协方差相等与不等时的两类情况，分类效果很好-Bayesian classifier design, including equal and unequal covariance of two categories, very good classification performance Platform: |
Size: 1024 |
Author:邓俊俊 |
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Description: Bayesian classifier experimental pattern recognition programs used in the experimental program Platform: |
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Author:PG_usthb |
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Description: 1 通过实验，掌握多元正态分布的最大似然估计；
2 掌握多元正态分布下的最小错误率的贝叶斯分类；
3 对其他的参数估计有更深的认识。
-1 experiment, master multivariate normal distribution maximum likelihood estimation 2 multivariate normal distribution under the minimum control error rate Bayesian classifier 3 on the other have a better understanding of parameter estimation. Platform: |
Size: 62464 |
Author:李岩 |
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Description: 朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出，致使其性能有所下降。通过引入关联规则，从两方面来改善朴素贝叶斯分类的性能。一方面，通过对关联规则的挖掘，发现条件属性之间的关联关系，并且利用这种关联关系弱化朴素贝叶斯的独立性假设；另一方面，通过关联规则的置信度，给朴素贝叶斯加权。 -Naive Bayesian classifier is a simple and efficient classification model, the conditional independence assumption, however, rarely in the real world, resulting in decreased performance. Through the introduction of association rules, two ways to improve the performance of naive Bayesian classifier. On the one hand, by association rule mining, found the association between condition attributes and use this association weakened Bayesian independence assumption the other hand, by association rule confidence, to the simple Bayesian Alaska right. Platform: |
Size: 1310720 |
Author:张广明 |
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