搜索资源列表
ID3code
- id3算法进行决策树生成 以信息增益最大的属性作为分类属性,生成决策树,从而得出决策规则。id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法,想必对大家会有所帮助!-id3 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, Thus,
Theclassicalid3
- id3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法,想必对大家会有所帮助!id3算法进行决策树生成 以信息增益最大的属性作为分类属性,生成决策树,从而得出决策规则。-id3 the most comprehensive source Decision Tree classic version. Id3 decision tree and the achievement test data. I d3 a useful data mining al
C4.5算法源程序
- C4.5算法进行决策树生成 以信息增益最大的属性作为分类属性,生成决策树,从而得出决策规则。-C4.5 decision tree algorithms to generate information gain the greatest attribute as a classification attributes, generate decision tree, and came to decision-making rules.
entropy
- 信息论中的信息 ppt课件 自信息量 信息增益 熵 内容不错 推荐-Since the amount of information entropy information gain
C4_5.m
- his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the poss
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- D3的源码决策树最全面最经典的版本.id3决策树的实现及其测试数据.id3 一个有用的数据挖掘算法,想必对大家会有所帮助!id3算法进行决策树生成 以信息增益最大的属性作为分类属性,生成决策树,从而得出决策规则。-D3 of the source tree the most comprehensive version of the most classic. Id3 decision tree and its test data. Id3 a useful data mining algorit
ID3
- The algorithm ID3 (Quinlan) uses the method top-down induction of decision trees. Given a set of classified examples a decision tree is induced, biased by the information gain measure, which heuristically leads to small trees. The examples are given
Entropy
- 一个计算信息熵的完整功能类。设计互信息,熵,信息增益,条件熵等等功能。-A calculation of the full functionality of class information entropy. Design of mutual information, entropy, information gain, conditional entropy and more.
DecisionTree
- decision tree calculate information gain
matlab_Lab3
- information gain calculation in Matlab
ParInfoGain
- ParInfoGain - Computes parallel information gain and gain ratio in Matlab using the Matlab Parallel Computing Toolbox or the Distributed Server (if available) Information gain is defined as: InfoGain(Class,Attribute) = H(Class) - H(Class | At
TestID3
- 决策树算法部分代码(从文件中读数据并计算信息增益)-Part of the code of the decision tree algorithm (data read from the file and calculate the information gain)
ID3-CSharp
- This my implementation of ID3 algorithm. The algorithm ID3 (Quinlan) uses the method top-down induction of decision trees. Given a set of classified examples a decision tree is induced, biased by the information gain measure, which heuristically lead
onTextCategorization
- 本文比较研究了在中文文本分类中特征选取方法对分类效果的影响。考察了文档频率DF、信息增 益IG、互信息MI、V2分布CHI 四种不同的特征选取方法。采用支持向量机(SVM) 和KNN两种不同的分类 器以考察不同抽取方法的有效性。实验结果表明, 在英文文本分类中表现良好的特征抽取方法( IG、MI 和 CHI)在不加修正的情况下并不适合中文文本分类。文中从理论上分析了产生差异的原因, 并分析了可能的 矫正方法包括采用超大规模训练语料和采用组合的特征抽取方法。最后通过实验验证组合特征
xinxizengyi
- 此程序主要为特征提取中的改进算法,信息增益,采用的是python写的-The procedure for feature extraction in the improved algorithm, information gain, is written in python
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
informationgain
- 完全的信息增益比代码,有注释。已运行。可进行信息熵,信息增益运算-Complete information gain ratio code, annotated.
DecisionTreeID3
- 决策树ID3算法的MATLAB程序,这里采用信息增益的方法.-MATLAB program of Decision Tree Algorithm ID3,by the information gain.
InformationGain
- 使用java实现的信息增益算法,附带了一些训练样本,已经进行了分词-Java algorithm using information gain realized, with some training samples have been carried out participle
c4.5
- C4.5是机器学习算法中的另一个分类决策树算法,它是基于ID3算法进行改进后的一种重要算法,相比于ID3算法,改进有如下几个要点:用信息增益率来选择属性.-C4.5 decision tree algorithm is another classification machine learning algorithm, which is based on ID3 algorithm is an important algorithm improved, compared to the ID3 a