搜索资源列表
ID3java
- 决策树，ID3算法，供大家参考学习，相互交流，相互帮助，共同进步-Decision Tree ID3 Algorithm with extremely detail comments, refering to the book of Data Minning Concepts and Techniques. Do comprehend the content from Page 185 to Page 196 in this book. Developed in MyEclipse7.0 Tes
DTree-C4.5
- ID3 决策树的实现 数据挖掘 Dtree4.5-ID3 decision tree data mining Dtree4.5
111
- C4.5算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法. -C4.5algorithm is a kind of machine learning algorithm of decision tree classification algorithm, the algorithm is the core of ID3 algorithm.
decisiontree1
- 使用sql server中的MDX语言直接对数据进行挖掘，采用ID3决策树算法-ID3 decision tree
jiqixuexi
- 机器学习的10大经典算法（决策树算法、ID3算法、k均值算法等等）-10 classic algorithms in machine learning
BIID3_javaa
- 基于决策树的数据挖掘算法，是很不错的的Java版的ID3算法，大家可以看看。 -Decision tree-based data mining algorithms is a very good Java version of the ID3 algorithm, we can see.
ID3_matlab_example
- matlab,数据挖掘决策树（ID3）算法例程-an example of ID3 base on matlab
MY_ID3
- 这是用C++实现的ID3决策树，决策树算法是非常常用的分类算法，是逼近离散目标函数的方法，学习得到的函数以决策树的形式表示。-ID3 decision tree, the decision tree algorithm implementation in C++ is a very commonly used classification algorithms, approximation discrete objective function, the learning function to
DecisionTree
- 决策树的简单实现，使用ID3作为属性度量选择，测试数据为Jiawei Han数据挖掘书本第六章购物篮数据-Simple implementation of the decision tree using ID3 as an attribute metric selection, test data Jiawei Han data mining the the books Chapter VI basket data
tree-algorithm
- 基于ID3理论的二叉树决策树算法的C++实现-Implemented decision tree algorithm, based on the the ID3 theory of binary tree C++
RandomForest
- ID3决策树+随机森林算法生成决策森林，采用投票机制进行决策；有训练数据aaa和测试数据bbb；非常适合机器学习的初学者-The ID3 the+ random forest algorithm to generate decision forests voting mechanism for decision-making training data the the aaa and test data bbb ideal for machine learning beginners
matlab_ID3
- 决策树的ID3算法的源代码，最后是其实验数据，运行很简单！-ID3 decision tree algorithm source code, and finally the experimental data, the run is very simple!
DecisionTree
- 利用ID3算法，实现决策树的源码。可以在VS2008环境下直接运行。-Use ID3 algorithm, the source tree. VS2008 environment can be run directly.
C4_5
- C4.5算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法. 分类决策树算法是从大量事例中进行提取分类规则的自上而下的决策树. -C4.5 algorithm is a machine learning algorithm, a classification decision tree algorithm, the core algorithm is ID3 algorithm classification tree algorithm is extracted from
DTree
- 一个实现分类决策树算法的系统。ID3算法和C4.5算法。-A decision tree algorithm to achieve classification system. ID3 and C4.5 algorithms.
C4.5
- C4.5 算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法. C4.5算法继承了ID3算法的优点，并在以下几方面对ID3算法进行了改进： 1) 用信息增益率来选择属性，克服了用信息增益选择属性时偏向选择取值多的属性的不足； 2) 在树构造过程中进行剪枝； 3) 能够完成对连续属性的离散化处理； 4) 能够对不完整数据进行处理。 C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描
ID3_Matlab
- ID3的Matlab实现，其中包含了四个源码文件，熵函数、类别熵函数、递归建立决策树部分、主程序。-ID3 using Matlab
Classifier
- 实现ID3 决策树算法，并使用MATLAB自带的工具箱函数画出决策树生成相应的规则-Achieve ID3 decision tree algorithm, and using MATLAB toolbox function that comes with the decision tree to generate the appropriate rules to draw
DM6_decision_tree
- 实现ID3决策树算法，可以实现基本眜分类，最终的决策树是以结构体存放的-ID3 decision tree
tree
- 决策树分类的各种代码,包括ID3、c4.5等等，有界面可以运行-Various code of decision tree classification, including ID3, C4.5 and so on, there are interface can run