搜索资源列表
C4_5
- 数据挖掘算法，分类树的C4.5算法，用于模式分类-data mining algorithms, the C4.5 classification tree algorithm for pattern classification
C4_5
- 用matlab语言写的C4.5算法,用于模式分类
chuojuwajujishusuanfamatlab
- 数据挖掘部分算法的matlab实现 C4_5 大家可以共同研究研究
C4_5.rar
- 决策树C45，用matlab编写的，数据挖掘算法。,Decision Tree C45, prepared by matlab, data mining algorithms.
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
C4_5
- 这是一个用matlab编写的C4.5算法的实现-this is a classification of machine learing,C4.5,using matlab
C4_5
- C4.5算法 matlab实现的，感觉还可以，大家可以看看！-the implementation of C4.5 using matlab
C4_5
- matlab源代码 C4.5决策树算法的matlab实现-C4.5
C4_5
- 数据挖掘的 MATLAB 算法源代码 希望有用-MATLAB algorithm for data mining source code hope helpful
jueceshu
- 从网络上搜集的决策树源码，包括C4_5、ID3、CART_iris三个源码，供大家一起学习研究。-Decision tree collected from the network source, including C4_5, ID3, CART_iris 3 source for study and research with everyone.
C4_5
- c4.5 a classification
C4_5
- 一种关于c4.5的简单matlab编程。-A kind of simple matlab program on c4.5.
C4_5
- C4.5决策树源代码，直接是matlab源代码-C4.5 decision tree source code matlab source code is directly
C4_5
- matlab实现决策树C4.5算法,首先利用训练数据创建决策树，再用测试数据对决策树进行剪枝。-C4.5 decision tree algorithm matlab realize, first use training data to create decision trees, and then test data for decision tree pruning.
C4_5
- C4.5决策树算法，可以进行数据分类，是数据挖掘的经典算法-C4.5 decision tree algorithm, data classification, is a classic data mining algorithm
C4_5
- 该源码使用C4.5算法进行分类，该程序为matlab源码。-Classify using Quinlan s C4.5 algorithm
C4_5
- 是关于C4.5算法，里面有C4.5的一些MATLAB所用的知识类容-it is ablout C4.5
C4_5
- 应用Matlab工具编写程序进而实现决策树C4.5分类算法-Use Matlab to make desicion tree
rough-set-codes
- 这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的