搜索资源列表
Cart
- 实现决策树分类训练试验。 源自c4.5，在windows下用C++实现,简洁好用。用户只需要构建好特征说明文件，并选择一些参数既可以进行试验。
matlab
- 决策树C4.5和CART算法的m源码 -CART decision tree algorithm C4.5 and the source m
five-algrithms
- 資料挖掘的五個最熱門演算法,包括adaboost,C4.5,CART,ID3,Fuzzy_k means等.-The most popular five algrithms applied in Data mining
DecisionTrees
- this decision tree ID3 algorithm, this algorithm is one of decision tree algorithm like cart, chaid, c4.5, etc-this is decision tree ID3 algorithm, this algorithm is one of decision tree algorithm like cart, chaid, c4.5, etc
10Algorithms-08
- This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
10Algorithms-08
- This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms
test_draworb0
- 高级信息提取 基于专家知识的决策树分类：规则获取（经验总结、数据挖掘如c4.5 cart算法）、规则定义以及构建决策树 -Advanced information extraction based on expert knowledge of the decision tree classification: the rules to get (lessons learned, data mining algorithms such as c4.5 cart), definit
test_object
- 高级信息提取 基于专家知识的决策树分类 -Advanced information extraction based on expert knowledge of the decision tree classification: the rules to get (lessons learned, data mining algorithms such as c4.5 cart), definitions and rules to build decision trees
10-da--suanfa
- 讲述了最著名的十大数据挖掘算法，经典资料，国际权威的学术组织the IEEE International Conference on Data Mining (ICDM) 2006年12月评选出了数据挖掘领域的十大经典算法：C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.-About the top ten most famous data mining algorithms, the
dataming
- 介绍数据挖掘的10种主要算法及其应用 一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。 -Top 10 algorithms in data mining his paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006:
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.
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
DT
- 人工智能方面的决策树算法，包括C4.5 ID3 CART三种评判标准实现方式 -Artificial intelligence decision tree algorithms, including three kinds of criteria C4.5 ID3 CART implementation
weka机器学习十大算法
- 对机器学习领域的十个经典算法进行了详细介绍，包括：AdaBoost、Apriori、C4.5、CART、EM、K-means、kNN、PageRand、SVM和朴素贝叶斯(Ten classical algorithms in machine learning domain are introduced in detail, including AdaBoost, Apriori, C4.5, CART, EM, K-means, kNN, PageRand, SVM and Nave Baye
classical-machine-learning-algorithm-master
- bayesian, k-means, knn, SVM, The Apriori algorithm, expectation-maximization(EM), C4.5, page rank, AdaBoost, CART
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