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classificiation-algorithm-overview
- 机器学习领域经典分类算法综述，包括Decision Tree（ＩＤ３、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法），三种典型贝叶斯分类器（朴素贝叶斯算法、TAN算法、贝叶斯网络分类器），k-近邻 、 基于数据库技术的分类算法（ MIND算法、GAC-RDB算法），基于关联规则（CBA:Classification Based on Association Rule）的分类（Apriori算法），支持向量机分类，基于软计算的分类方法（粗糙集（rough set）、遗传
C4_5Classifier
- c4.5 clasifier for the classification of the vedios.
C4.5决策树实现
- 可以用vs2010打开，可以用vs2010打开
c45-with-hadoop
- This a demo of Implementation of C4.5 Algorithm using Hadoop MapReduce frame work. c4.5 using java-This is a demo of Implementation of C4.5 Algorithm using Hadoop MapReduce frame work. c4.5 using java
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
tree
- 数据挖掘-决策树-c4.5算法的java代码实现-Data Mining- Decision Tree algorithm java code-c4.5
decisiontree
- 数据挖掘中决策树的java实现，c4.5算法的java实现-Java implementation of the decision tree data mining, c4.5 algorithm to achieve the java
aa
- 决策树算法的实现，运用java语言，实现c4.5算法-Achieve decision tree algorithm, using java language, to achieve c4.5 algorithm
c45.tar
- C4.5决策树源码， C语言，文档说明详细，欢迎使用。-C4.5 Decision tree
C5.0-------
- C4.5决策树算法，C++算法，通过仔细阅读，可以很方便的了解跟学习决策树算法。-C4.5 decision tree algorithm, C++ algorithm, by reading carefully, you can easily understand the decision tree algorithm with learning.
c45
- 决策树算法c4.5 C语言实现以及命名规则和构建工具开发集合-C4.5 decision tree algorithm C language, naming and build tools set
C4.5
- 改进型的决策树算法，特别的实用，欢迎大家下载，也可以用到论文算法中-Improved decision tree algorithm, especially useful, welcome to download, you can also use paper algorithm
C4_5
- 决策树C4.5程序设计，非常实用且方便，欢迎大家学习，借鉴。-C4.5 decision tree program design, very practical and convenient, welcome everyone to learn from.
10.1.1.1.8430
- NeC4.5: Neural Ensemble Based C4.5 Zhi-Hua Zhou, Member, IEEE, and Yuan Jiang Abstract—Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability.
myc45java
- It is C4.5 algorithm written in java. C4.5 is one of the decision tree algorithms in data mining.
c45
- c4.5 implementation. Hope you can find it helpful
adaboost
- Now, you ought to implement the AdaBoost.M1 and AdaBoost.M2 algorithms. These algorithms are two versions of the AdaBoost algorithm for handling the Problems with more than two classes. You must first read the paper “Experiments with a New Boosti
MatlabCode
- matlab code for C4.5 decision tr-matlab code for C4.5 decision tree
C4_5
- 这是分类树的C4.5算法分类，算法比较简单，但是运行高效，可以对图像进行分类-This is the classification tree C4.5 algorithm classification, the algorithm is relatively simple, but the operation is efficient, you can classify the image
trees
- 机器学习中的c4.5树的源代码，经测试，可以使用-The source code tree c4.5 in machine learning, through the test, can be used