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: 1000 |
Author:李思 |
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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: 基于决策树的n则交叉验证分类器
（决策树程序直接调用matlab中的）
crossvalidate.m N则交叉验证程序，N可选
NDT.mat 含9个国际公认标准数据集，已做过标么处理，可直接使用
专业-n Based on Decision Tree is cross-validation classification (decision tree directly call the Matlab) cr ossvalidate.m N is cross-validation procedures, N optional NDT.mat with nine internationally recognized standard data sets, what has been done superscript, direct access to professional Platform: |
Size: 235520 |
Author:hehuixin |
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Description: 用matlab语言写的ID3(Quinlan s ID3 classification tree algorithm)算法,用于模式分类-Matlab language used to write the ID3 (Quinlan s ID3 classification tree algorithm) algorithm for pattern classification Platform: |
Size: 1024 |
Author:罗升阳 |
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Description: vc++下的决策树cart算法，最为经典的算法之一。-vc++ under the cart of the decision tree algorithm, one of the most classic algorithm. Platform: |
Size: 2048 |
Author:and |
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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: 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和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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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: 对已有图像进行区域识别，画出决策树，并在测试图像上进行测试。-The image of the existing regional recognition, draw the decision tree, and the test images for testing. Platform: |
Size: 5120 |
Author:chang |
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Description: c4.5经典算法，实现决策树分类功能，可以对连续数值和离散数值实现很好的分类，并有剪枝功能-c4.5 classic algorithms, to achieve the decision tree classification, can be continuous and discrete numerical values to achieve good classification, and a pruning function Platform: |
Size: 2048 |
Author:zhangyi |
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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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Description: ID3决策树算法，实现不同条件下数据的分类-ID3 decision tree algorithm, the classification of data under different conditions Platform: |
Size: 70656 |
Author:孙东 |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms, and two and a support vector machine decision tree classification algorithm of moving objects. Platform: |
Size: 544768 |
Author:piano |
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