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: 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: 包含了很多分类算法，有SVM，knn，决策树等，还有文档说明-Contains a lot of classification algorithms, there is SVM, knn, decision tree and so on, have documented Platform: |
Size: 990208 |
Author:来海锋 |
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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: The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 " Platform: |
Size: 1024 |
Author:loris nanni |
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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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