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his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the poss
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quilan的决策树c4.5-r8的windows版本-quilan decision tree c4.5-r8 of the windows version
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C4.5是决策树的经典算法 C4.5 归纳学习是完全自动的学 习算法，所需要做的是选取有用的特征，构建实例数据库供它学习-C4.5 decision tree is the classic C4.5 inductive learning algorithm is completely automatic learning algorithm, what needs to be done is to select useful features, build databases for its e
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决策树的java代码，有id3和c4.5的-Decision tree java code, there' s id3 and c4.5
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C4.5决策树源代码，直接是matlab源代码-C4.5 decision tree source code matlab source code is directly
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matlab实现决策树C4.5算法,首先利用训练数据创建决策树，再用测试数据对决策树进行剪枝。-C4.5 decision tree algorithm matlab realize, first use training data to create decision trees, and then test data for decision tree pruning.
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Quinlan的决策树程序C4.5（M语言编写）-the program for decision tree C4.5 purposed by Quinlan
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J48 (unpruned or pruned C4.5 decision tree algorithm) WEKA project
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C4.5决策树算法，可以进行数据分类，是数据挖掘的经典算法-C4.5 decision tree algorithm, data classification, is a classic data mining algorithm
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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
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用c++编写的决策树识别，数据来源为csdn上一位大神的经典之作-Classic written c++ decision tree identification, data sources from csdn
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决策树分类 通过读取数据 求信息增益率选择最好的分离属性-Decision tree classification by reading the data and information gain ratio to select the best separation properties
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The aim of this article is to show a brief descr iption about the C4.5 algorithm, used to create Univariate De- cision Trees. We also talk about Multivariate Decision Trees, their process to classify instances using more than one attribute per node i
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决策树算法_C实现，主要是数据挖掘领域。-_C Decision tree algorithm to achieve
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C++编写的C4.5决策树程序，为数据挖掘基础算法。
网址为:http://www.cnblogs.com/michaelGD/archive/2012/11/14/2770758.html-C++ written C4.5 decision tree program, the foundation for the data mining algorithms. Site at: http://www.cnblogs.com/michaelGD/archive/2012/11/14/277
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C4.5 算法是机器学习算法中的一种分类决策树算法,其核心算法是ID3算法. C4.5算法继承了ID3算法的优点，并在以下几方面对ID3算法进行了改进：
1) 用信息增益率来选择属性，克服了用信息增益选择属性时偏向选择取值多的属性的不足；
2) 在树构造过程中进行剪枝；
3) 能够完成对连续属性的离散化处理；
4) 能够对不完整数据进行处理。
C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描
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C4.5决策树算法java实现,未实现截枝部分-decision tree algorithm
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决策树C4.5 用于分类与预测的算法 简洁方便-decision tree C4.5 For classification and prediction algorithm Simple and convenient
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C4.5决策树源码， C语言，文档说明详细，欢迎使用。-C4.5 Decision tree
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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.
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