搜索资源列表
matlab_bayes_classifier
- 使用matlab编写的bayes分类器,朴素贝叶斯分类器-Prepared using bayes classifier matlab
BayesianClasser
- 贝叶斯分类器matlab版,可以用于图像分类,检索,模式识别相关工作-bayes classifier is useful to image classify
naivebayes
- 模式识别中朴素贝叶斯分类器,实习数据的良好分类技术-naive bayesian classification
judger
- 最小错误率和最小风险贝叶斯分类器,附带示例数据-Minimum error rate and minimum risk Bayes classifier, with sample data
Bayes_Classify
- 贝叶斯分类器,利用后验概率,对已知属性对象进行分类-Bayesian classifier, the use of posterior probability of the known properties of the object classification
work_for_pattern_recognition
- 通过设计线性分类器;最小风险贝叶斯分类器;监督学习法分层聚类分析;K-L变换提取有效特征,设计支持向量机对给定样本进行有效分类并分析结果。-By designing a linear classifier minimum risk Bayes classifier supervised learning method hierarchical cluster analysis K-L transform to extract efficient features, designed to
program
- matlab 贝叶斯分类器+bayesian 程序-Bayesian classifier matlab program+ bayesian
NBC
- 用C#编的朴素贝叶斯分类器。希望大家能用得上-A Naive Bayesian Classifier in C#
classifier
- 两类二维相关正态分布条件下的最小错误率贝叶斯分类器,基于最小风险的贝叶斯分类器,Parzen窗法非参数估计分类器程序,Fisher线性判别法分类器程序。-Under normal conditions two types of two-dimensional correlation of minimum error rate of Bayesian classifier, the minimum risk-based Bayesian classifier, Parzen window meth
NaiveBayesianclassifier
- 朴素贝叶斯分类器识别鼠标输入的字母A-J-Naive Bayesian classifier to recognize the letters AJ mouse input
Bayes
- 传统贝叶斯分类器,最小错误率贝叶斯分类器、最小风险贝叶斯分类器-Traditional Bayesian classifier, the minimum error rate classifier, minimum risk Bayes classifier
bayes_classifier
- 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.
bayes
- 贝叶斯分类器的设计与实现,非常好的应用程序,能够在其上面实现人脸识别-Bayesian classifier design and implementation of a very good application, face recognition can be achieved in the above
Bayes
- 贝叶斯分类器,基于最小错误率的贝叶斯分类器-Bayes
FullBNT-1.0.4
- 比较全面的贝叶斯工具箱,包含贝叶斯分类器等的设计等-bayes tools box
bayes
- 朴素贝叶斯分类器包括了停用词的处理,结果是很不错的-Naive Bayesian classifier
贝叶斯分类器
- 贝叶斯分类器设计,分参数已知和参数未知两种情况,含最大似然参数估计代码
模式识别
- 简单的贝叶斯分类器,实现基于身高体重的男女性别分类(Simple Bias classifier)
Bayes classifier
- 基于贝叶斯分类器的数据处理与MATLAB实现(Data processing based on MATLAB implementationof Bayes classifier)
贝叶斯判决
- 假定某个局部区域细胞识别中正常w1和非正常w2 两类先验概率分别为: 正常状态:P(w1)=0.9 ; 异常状态:P(w2)=0.1 。 现有一系列待观察的细胞,其观察值为: -2.67 -3.55 -1.24 -0.98 -0.79 -2.85 -2.76 -3.73 -3.54 -2.27 -3.45 -3.08 -1.58 -1.49 -0.74 -0.42 -1.12 4.25 -3.99 2.88 -0.98 0.79 1.19 3.07 两类的类条件概率符合正态分布