Search - matlab tree classification

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[Other resource] CART_iris DL : 0: 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.
Update : 2008-10-13 Size : 1000byte Publisher : 李思
[matlab] C4_5 DL : 0: 数据挖掘算法，分类树的C4.5算法，用于模式分类-data mining algorithms, the C4.5 classification tree algorithm for pattern classification
Update : 2025-02-19 Size : 2kb Publisher : sah
[matlab] CART_iris DL : 0: 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.
Update : 2025-02-19 Size : 1kb Publisher : 李思
[AI-NN-PR] crossvalidate DL : 0: 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
Update : 2025-02-19 Size : 230kb Publisher : hehuixin
[matlab] ID3 DL : 0: 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
Update : 2025-02-19 Size : 1kb Publisher : 罗升阳
[Mathimatics-Numerical algorithms] cart DL : 0: 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.
Update : 2025-02-19 Size : 2kb Publisher : and
[AI-NN-PR] id3matlab DL : 0: 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
Update : 2025-02-19 Size : 2kb Publisher : tian
[JSP/Java] C4.5 DL : 0: 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.
Update : 2025-02-19 Size : 1kb Publisher : xinyuanwo
[matlab] PatternRecognition DL : 0: 1.Fisher分类算法 2.感知器算法 3.最小二乘算法 4.快速近邻算法 5.K-近邻法 6.剪辑近邻法和压缩近邻法 7.二叉决策树算法-1.Fisher Classification Algorithm 2. Perceptron algorithm 3. Least-squares algorithm 4. Fast nearest neighbor 5.K-neighbor method 6. Clips neighbor neighbor method and compression method 7. Binary Decision Tree Algorithm
Update : 2025-02-19 Size : 8kb Publisher : wct
[matlab] mill DL : 0: 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
Update : 2025-02-19 Size : 967kb Publisher : 来海锋
[AI-NN-PR] CART DL : 0: 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.
Update : 2025-02-19 Size : 1kb Publisher : 肖箫
[Mathimatics-Numerical algorithms] IDE DL : 0: 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 "
Update : 2025-02-19 Size : 1kb Publisher : loris nanni
[Graph program] HW5 DL : 0: 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.
Update : 2025-02-19 Size : 5kb Publisher : chang
[AI-NN-PR] ID3 DL : 0: 决策树算法经典的ID3算法用于决策树规则学习等等在规则学习以及分类中有重要的作用-classic decision tree mode for classification and rule learning
Update : 2025-02-19 Size : 2kb Publisher : zhupengfei
[Other] datamining DL : 0: 一份关于数据挖掘的入门资料，进行了数据挖掘的基本介绍还介绍了聚类，关联规则，决策树，神经网络-data mining, cluster, classification, Decision tree, Neural network
Update : 2025-02-19 Size : 409kb Publisher : Erin
[Mathimatics-Numerical algorithms] C4_5 DL : 0: 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
Update : 2025-02-19 Size : 2kb Publisher : zhangyi
[matlab] ID3 DL : 0: 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.
Update : 2025-02-19 Size : 2kb Publisher : 王剑亭
[matlab] ID3(MATLAB) DL : 0: 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
Update : 2025-02-19 Size : 69kb Publisher : 孙东
[matlab] CKPCA-HOG-SVM DL : 0: 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.
Update : 2025-02-19 Size : 532kb Publisher : piano
[Other] C45_matlab DL : 0: matlab code for c 4.5 tree classification
Update : 2025-02-19 Size : 16kb Publisher : Aziziz

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