搜索资源列表
nativeBayes
- 朴素贝叶斯算法的java代码实现与实际例子-native beyes
9215JavaMail
- JAVA垃圾邮件过滤系统，贝叶斯过滤算法的经典算法模式。-JAVA spam filtering system, the classical algorithm of Bayesian filtering algorithm.
Bayes
- 用java语言实现的朴素贝叶斯算法。可以根据输入属性值来判断目标属性的值。-This is the code about Naive Bayes.
TestNaiveBayes
- 采用朴素贝叶斯算法自动处理大量数据，Java语言编写。数据见TXT文件。-Using naive Bayesian algorithm to automatically deal with a large number of data, Java language. See data TXT file.
learning
- 机器学习代码，贝叶斯java的实现代码。其他还用到了中文语言的分词器。有注释，易于阅读。-Machine learning code, Bayesian java implementation code. Other languages also uses the Chinese word breaker. Notes, easy to read.
Naive-Bayes
- Naive Bayes 朴素贝叶斯算法的实现-Naive Bayes
test1
- 朴素贝叶斯分类算法，用于进行文本分类，自带训练集-Naive Bias classification algorithm with training set
CLASSIFICATION-with-newsgroup
- 多文本分类，KNN和朴素贝叶斯算法，英文文本，-Text categorization, KNN and naive bayes algorithm, the English text,
KKKK
- 改进了一种基于贝叶斯算法的短信过滤算法.首先介绍了短信特征选取，包括预处理、特征项选取、权重表示；在贝叶斯文本分类算法基础上做出改进，详细讲述了如何对短信样本预处理、特征项选取、提高分类正确率的方法，并且通过测试实验成功得出结果.-Improved algorithm based on Bayesian filtering algorithm first introduced the SMS text messaging feature selection, including preproce
JAbbbb
- 改进了基于朴素贝叶斯的java实现短信过滤算法-java achieve SMS filtering .............................................. ...........................
weka
- 机器学习调用weka的jar包实现的源码，包含朴素贝叶斯,决策树,ID3,以及特征选择的源码，数据集使用weka的数据集，需要使用arff文件读入。-Weka machine learning to call the jar package implements the source, including Naive Bayes, decision trees, ID3, and features selected source dataset weka data set, you need t
mssb
- 使用win7 java最小错误率贝叶斯决策规则。-Minimum error rate Bayesian decision rule
bayes
- 关于贝叶斯网络的JAVA源代码 应用于分类问题的学习和研究-Bayesian network on the JAVA source code
LDA代码分析
- 对文本用LDA进行分类，LDA（Latent Dirichlet Allocation）是一种文档主题生成模型，也称为一个三层贝叶斯概率模型，包含词、主题和文档三层结构。(The text is classified with LDA)
Demo
- 建立了一个贝叶斯网络，是采用Netica公司的软件进行的(build a Bayesian Net)
naive_bayes_demo
- 算是是朴素贝叶斯的java实现，数据集已在文件包中，欢迎有错指出(naive baye demo including data Source implement use java .)
rseslib-3.0.4-src
- 包含很多知名算法实现，支持向量机，决策树，粗糙集，贝叶斯分类器等，适合学术研究，短评论意见挖掘，文本分类等(It includes many well-known algorithm implementation, support vector machine, decision tree, rough set, Bias classifier, etc., which is suitable for academic research, short comment mining, text c