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Description: 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。
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Size: 139967 |
Author: 胡伟湘 |
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Description: 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。-Realize decision tree classifier trained pilot. From c4.5, in windows using C++ Realize, concise easy to use. Users only need to build a good feature of the documentation, and select some of the parameters can be tested.
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Size: 139264 |
Author: 胡伟湘 |
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Description: 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
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Size: 465920 |
Author: sang |
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Description: 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
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification,
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Size: 622592 |
Author: sukmawati |
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Description: 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
are among the most influential data mining algorithms in the research community.With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
reviewcurrent and further research on the algorithm. These 10 algorithms cover classification
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Size: 635904 |
Author: ParisM |
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Description: 决策树C4.5和CART算法的m源码
-CART decision tree algorithm C4.5 and the source m
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Size: 4096 |
Author: 王麦 |
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Description: 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), definitions and rules to build decision trees
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Size: 2048 |
Author: 陈红 |
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Description: 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
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Size: 2048 |
Author: 陈红 |
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Description: 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
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Size: 354304 |
Author: scott |
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Description: （经典聚类算法）
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.
不仅仅是选中的十大算法，其实参加评选的18种算法，实际上随便拿出一种来都可以称得上是经典算法，它们在数据挖掘领域都产生了极为深远的影响。-(Classical clustering algorithm)
International authoritative academic organization of the IEEE International Conference on Data Mining (ICDM) in December 2006 selected the top ten of the field of data mining algorithm: the C4.5, k-Means, SVM, of Apriori, the EM, the PageRank, AdaBoost, kNN , the Naive Bayes, and the CART.
Not just the selected 10 algorithms, in fact, participate in the selection of 18 kinds of algorithms, in fact, easily come up with one can be called a classical algorithm in the field of data mining, they have had far-reaching impact.
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Size: 3922944 |
Author: 赵鑫维 |
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Description: 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 classical information, the international authority of the academic organization of the IEEE International Conference on Data Mining (ICDM) 2006, selected the top ten of the field of data mining algorithms: the C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
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Size: 57344 |
Author: 吴贵锋 |
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Description: 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: C4.5,k-Means, SVM,
Apriori, EM, PageRank, AdaBoost,kNN, Naive Bayes, and CART. These top 10 algorithms
are among the most influential data mining algorithms in the research community. With each
algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and
review current and further research on the algorithm. These 10 algorithms cover classification,
Platform: |
Size: 633856 |
Author: andyzygg |
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Description: 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.
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Size: 727040 |
Author: zhongrui |
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Description: 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）、遗传算法、模糊逻辑、人工神经网络算法）。-Classical machine learning classification algorithms overview。 Including Decision Tree (ID3, C4.5 (C5.0), CART, PUBLIC, SLIQ and SPRINT algorithm), three typical Bayesian classifier (Naive Bayes algorithm, TAN algorithm Bayesian network classifiers), k-nearest neighbor based classification algorithm (MIND algorithm, GAC-RDB algorithms) database technology, based on association rules (CBA: classification (Apriori algorithm) Classification Based on Association Rule), and support vector machine classification, classification method based on soft computing (rough sets (rough set), genetic algorithms, fuzzy logic, artificial neural network algorithm)....
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Size: 30720 |
Author: MM |
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Description: 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
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Size: 2997248 |
Author: 潘霏 |
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Description: 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 Bayes)
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Size: 4810752 |
Author: kmsj
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Description: bayesian, k-means, knn, SVM, The Apriori algorithm, expectation-maximization(EM), C4.5, page rank, AdaBoost, CART
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Size: 11264 |
Author: 莲66
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Description: 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 forest is given. This paper mainly analyzes the learning algorithm of decision tree: information gain and ID3, C4.5, CART tree, and then give the random forest.
Among the decision trees, there are 3 of the most important issues.
1. feature selection. Which is to choose which feature as the classification of a node;
2. the selection of eigenvalues. That is, how to divide the subtrees after the selection of the good features.
3. how to do the fitting of the decision tree?
The following questions are explained on the decision tree respectively. The decision tree is often the optimal feature of the recursive selection, and the training data are segmented according to the feature.)
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Size: 2114560 |
Author: ZJN27 |
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