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c4.5
- 决策树 c4.5 分类器, 用java语言写的.大家一起参考一下. -c4.5 decision tree classifier, using the java language. U.S. with reference.
Kode-Program-Algoritma-c4.5
- c4.5 is an algorithm used to generate a decision tree. c4.5 is an extension of Quinlan s earlier ID3 algorithm. The decision trees generated by c4.5 can be used for classification, and for this reason, c4.5 is often referred to as a statistical class
c4.5
- 基于c4.5的决策树算法，一种非线性分类器-Based on c4.5 decision tree algorithm, a nonlinear classifier
classifier
- 该源码实现了决策树c4.5算法，用于分类预测-This source realized the decision tree c4.5 algorithm used for classification prediction
adaboos
- 当弱分类器算法使用简单的分类方时,boosting的效果明显地统一地比bagging要好.当弱分类器算法使用c4.5时,boosting比bagging较好,但是没有前者的比较来得明显.-When the weak classifier algorithm using simple classification method, boosting the effect clearly uniformly better than bagging. When the weak classifier
c4.5-decision-tree-matlab
- c4.5分类器 支持向量机算法 文本分类 样本支持 核函数算法-c4.5 classifier SVM text classification algorithm sample support kernel function
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
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
id3决策树——new
- 这是一个ID3决策树分类的代码用于分类器的计算，大家下载类以后可以学习使用，ID3是一种比较古老的决策树，最新的有c4.5等(This is an ID3 decision tree classification code for classifier calculation, which can be used after downloading the class. ID3 is a relatively ancient decision tree, with the latest c4.5