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模式识别_贝叶斯分_类器
- 一个用来做模式识别的贝叶斯分类器-used as a pattern recognition Bayesian classifier
贝叶斯和Fisher分类器的matlab源程序
- 基于Bayes和fisher的分类器,便于对通一批数据比较两个分类器的分类结果标有注视,简单易懂,用于初学者入门使用
BayesClassifier.rar
- 贝叶斯分类算法,构造朴素贝叶斯分类器,对给定的中文文本进行分类,Bayesian classification algorithm, Naive Bayes classifier structure of a given Chinese text classification
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
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
Ionosphere_LR
- 电离层的贝叶斯分类器,使用matlab编程开发,具有正确率高的优点-Ionosphere Bayesian classifier, using the matlab program development, the advantages with the correct rate
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.
bayescode
- 一种自己设计的贝叶斯分类器,具有一定的参考价值-A kind of self-designed Bayesian classifier, with some reference value
PR02
- 介绍贝叶斯分类器的模式识别课程课件,很生动形象,适合初学者-Introduction Bayesian classifier pattern recognition program courseware, very vivid image, suitable for beginners
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
Mail
- 可连接163,126和qq邮箱,获取邮箱中的邮件,根据贝叶斯分类器的的学习识别获取到的垃圾邮件。-This program connects to 163, 126 or QQ mailbox to obtain the email, and recognizes the spam based on the learning result by Bayes Classifer.
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
Bayes
- 贝叶斯分类器的分类原理是通过某对象的先验概率,本文详细介绍贝叶斯分类器,使用贝叶斯分类器对样本进行训练分类,得到良好分类结果,并对分类结果进行分析。-Principle of Bayesian classifiers is through prior probability of an object, the paper describes Bayesian classifier, Bayesian classifier using the training sample classificat
SharpClassifier_Adult-master
- 利用朴素贝叶斯分类方法将UCI的adult数据集进行分类(The adult dataset of UCI was classified by the naive bayesian classification method)
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
- 这是一个用于贝叶斯分类器的源代码,请有需要的朋友看一下(This is the python source code used to describe baysion clustering. It can be combined with your actual needs.)
分类器评估及交叉验证_代码
- 内有鸢尾花数据的5折交叉验证实验代码,采用的分类器是贝叶斯分类器。(There is a 5-fold cross-validation experiment code for the iris data, and the classifier used is a Bayesian 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