搜索资源列表
Cart
- 实现决策树分类训练试验。 源自c4.5，在windows下用C++实现,简洁好用。用户只需要构建好特征说明文件，并选择一些参数既可以进行试验。
jueceshur8
- 是构造决策树分类器的一种算法，它是ID3算法的扩展。ID3算法只能处理离散型的描述性属性，而此算法还能够处理描述性属性是连续型的情况。
id3matlab
- Id3是最基础的决策树分类方法，是其他决策树分类方法的基础，这个是Id3分类方法的matlab 实现
DecisionTree
- 决策树分类，Your task for this project is to develop an system which performs a classification task with any various Decision Tree.
wajue
- 利用VC++实现决策树分类算法，已经测试过！可用
id3andc4.5javasourcecode
- 决策树分类中经典算法的ID3和Ｃ４．５代码公共包！
CTree
- 决策树分类C5.0算法的C语言实现源码！
r8
- 这是一个有关决策树分类器中C4.5算法的原程序-This is a decision tree classifiers on which the original algorithm C4.5 procedures
92 java版本
- 本程序是用java语言编写的数据挖掘分类算法中的决策树分类方法c4.5程序代码-this procedure is used java language classification of data mining algorithms decision tree classification code Bank
c4.5数据挖掘算法源代码，LINUX版本
- 本程序是用c语言编写的基于决策树分类方法的数据挖掘算法，它对测试集进行分类，挖掘出潜在的规则-this procedure is used to prepare the language c decision tree classification based on the data mining algorithm, which tests set for classification, tapping the potential rules
C50
- 功能强大的决策树分类算法，是C4.5的改进版本，但在精度，速度和内存开销上均有了很大的改进。目前由rulequest公司管理，其可执行程序版本为商业版本，此GPL许可的源代码对外发布。-Both C4.5 and C5.0 can produce classifiers expressed either as decision trees or rulesets. In many applications, rulesets are preferred because they are simp
matlabcart
- 决策树分类法，通过决策树分类方法将数据集分类，传统的数据挖掘方法。-Decision tree classification, classification by decision tree classification data sets, the traditional data mining methods.
matlab-code
- 小波分析 图像融合 决策树分类 matlab 源代码 -fusion image, classify
finalcode
- 决策树分类器的代码，用Python编写，用于各种分类训练-Code decision tree classifier, using Python, for a variety of classified training
ID3
- 自己写的， 决策树分类， machine learning project-ID3 classifitor
ID3
- 决策树分类算法的实现和性能测试,使用UCI Iris Data Set进行测试。-Implementation and performance testing of the decision tree classification algorithm
ID3
- 在VS2010平台上编写的ID3决策树分类算法，包含一个小的训练数据sample.text-VS2010 platform to write the ID3 decision tree classification algorithm, contains a small training data sample.text
sjwjfenlei
- 决策树分类器 非常好用 大家快来下载 真正的决策树分类器 -The decision tree classifier is very easy to use Come download real decision tree classifier
C4.5
- 决策树分类 通过读取数据 求信息增益率选择最好的分离属性-Decision tree classification by reading the data and information gain ratio to select the best separation properties
C4_5Cha
- matlab中粗糙集属性约简的决策树分类的方法以及如何建立和使用决策树代码-decision tree