搜索资源列表
DecisionTree
- 决策树的经典C4.5算法，基于VS2010，对学习人工智能的同学有帮助-C4.5 decision tree algorithm, based on VS2010
machine-learning-2
- 机器学习算法之C4.5与CART，经典的机器学习的外文资料，该资料描述详细，便于大家的学习。-The machine learning algorithm C4.5 and CART, the classical machine learning foreign language information, the information described in detail, easy to learn from everyone.
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
- 是一个介绍及其学习算法C4.5的文档，里面讲的比较详细-Is an introduction to its learning algorithm C4.5 of the document, which the more detailed
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
J48
- J48算法源代码,WEKA，C4.5算法源代码-J48 algorithm source code
JAVA-decisiontree
- 本程序由Java编写，运行前请确认您的电脑上已安装JDK1.7或以上版本并配置好JDK的系统环境变量。请使用Eclipse集成开发导入源代码-The Algorithm of Decision Trees: ID3 and C4.5
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
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
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
决策树与随机森林
- 给出对决策树与随机森林的认识。主要分析决策树的学习算法：信息增益和ID3、C4.5、CART树，然后给出随机森林。 决策树中，最重要的问题有3个: 1. 特征选择。即选择哪个特征作为某个节点的分类特征； 2. 特征值的选择。即选择好特征后怎么划分子树； 3. 决策树出现过拟合怎么办？ 下面分别就以上问题对决策树给出解释。决策树往往是递归的选择最优特征，并根据该特征对训练数据进行分割。(The understanding of decision tree and random
treePlotter
- 绘制ID3，C4.5,CRAT决策树算法的树型(Drawing the tree pattern of ID3, C4.5, CRAT decision tree algorithm)