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
target-detection-algorithm-
- 为克服传统目标识别方法在处理空间特征分布极为复杂的数据时的缺点，提出1 种基于决策树的多特征检测算法，并将其应用到基于视频的海上搜救目标检测中. 该算法首先提取图像中的颜色、亮度等信息，通过计算各特征的信息增益建立决策树，将搜救目标检测问题分解成3 层决策树分类问题. 实验表明，该算法能够提高多特征目标检测的效率，在救生艇、筏等海上搜救目标检测的应用中取得较好的结果.-Characteristics to overcome the traditional target recognition m
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
IG
- 文本分类中特征提取的代码。采用信息增益法，对文本的空间向量模型能达到有效降维。文件的输入形式必须是词号-词频形式。- Text Categorization feature extraction code. Using information gain method, the vector space model of the text to achieve effective dimensionality reduction. Enter the file must be in the f
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.