Description: 数据挖掘中的决策树C4.5算法的实现,用matlab实现-Data Mining Decision Tree Algorithm of C4.5, using Matlab to achieve Platform: |
Size: 2048 |
Author:利军 |
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Description: matlab数据挖掘算法。实用cart决策树进行分类，可识别多类。decision tree algorithm, classification.-Matlab data mining algorithms. Practical cart decision tree classification, identification number category. Decision tree algorithm, the classification. Platform: |
Size: 1024 |
Author:李思 |
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Description: Id3是最基础的决策树分类方法，是其他决策树分类方法的基础，这个是Id3分类方法的matlab 实现-Id3 is the most basic decision tree classification method, other methods of decision tree classification, this classification method is id3 realize the matlab Platform: |
Size: 2048 |
Author:tian |
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Description: C5.0 决策树源码, 此算法要优于C4.5算法-C5.0 decision tree source, this algorithm is superior to C4.5 algorithm Platform: |
Size: 81920 |
Author:Jianfei Wu |
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Description: 决策树，很经典，不是一般的经典，你看看吧-Decision tree, it is classic, not an ordinary classic, you take a look at it Platform: |
Size: 641024 |
Author:朱朱 |
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Description: c4.5 关于决策树decision tree的matlab实现程序 -c4.5 decision tree decision tree on the realization of the matlab program Platform: |
Size: 4096 |
Author:凌风 |
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Description: 这是一个分类和回归树算法，它提供一种通用框架将各种各样不同的判定树实例化。-This is a classification and regression tree algorithm, which provides a common framework a wide variety of different decision tree instantiation. Platform: |
Size: 1024 |
Author:肖箫 |
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Description: 决策树算法的matlab实现，主要适用的是id3
算法思想-Decision Tree Algorithm to achieve the matlab main id3 algorithm is applicable to thinking Platform: |
Size: 6144 |
Author:fj |
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Description: 使用MATLAB语言完成的决策树算法。
里面有详细说明-Using the MATLAB language to complete the decision tree algorithm. Details inside Platform: |
Size: 87040 |
Author:老虎 |
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Description: 实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。-ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose of prediction. The decision tree training set of data according to the formation of the first decision tree, if the tree can not give the correct classification of all objects, then select a number of exceptions to the training set data, repeat the process until the correct decision set. Platform: |
Size: 2048 |
Author:王剑亭 |
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