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Description: * acousticfeatures.m: Matlab script to generate training and testing files from event timeseries.
* afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries.
* extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information.
* extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes.
* sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries.
* ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module.
?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module.
?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
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Size: 10081 |
Author: 陈延军 |
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Description: * acousticfeatures.m: Matlab script to generate training and testing files from event timeseries.
* afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries.
* extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information.
* extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes.
* sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries.
* ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module.
?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module.
?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
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Size: 10240 |
Author: 陈延军 |
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Description: 用matlab实现的算法，速度非常快，是knn的经典算法。-The realization of the algorithm using matlab, very fast, is a classic knn algorithm.
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Size: 1024 |
Author: 陈国章 |
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Description: MATLAB cross-validation tool for classification and regression v0.1
FEATURES:
+ K-fold cross validation.
+ Arbitrary train and prediction functions with parameters can be used.
+ Arbitrary loss function can be used.
+ Wrappers for KNN, SVM, GLM, robust regression and decision trees.
+ Wrappers for RMSE, MAD and misclassification loss functions.
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Size: 3072 |
Author: milk |
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Description: KNN Classification Code Matlab
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Size: 1024 |
Author: aya |
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Description: KNN Classification Code Matlab
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Size: 1024 |
Author: aya |
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Description: KNN Classification Matlab
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Size: 1024 |
Author: aya |
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Description: KNN Classification Code
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Size: 1024 |
Author: aya |
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Description: KNN Classification Code Matlab
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Size: 1024 |
Author: aya |
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Description: KNN Classification Code Matlab
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Size: 1024 |
Author: aya |
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Description: 给定一个样本集exset，样本数为M，每个样本点是N维向量，对于给定目标点d，d也为N维向量，要从exset中找出与d距离最近的k个点(k<=N)-knn code c++
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Size: 4096 |
Author: 吉祥如意 |
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Description: KNN the original method you can try to compare to another algorithm
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Size: 1024 |
Author: MooD |
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Description: knn 只是个函数实现 需要其它函数的引用-k-nearest neighbor
u need to import this function to other functions to run it
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Size: 1024 |
Author: qian |
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Description: The program k folds the data set and applies K-Nearest Neighbors algorithm to classify the images.
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Size: 1024 |
Author: SAI |
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Description: 实现一个knn 分类器。
使用欧氏距离作为度量距离
使用’my_order.m’函数脚本获得某个点最近的K 个邻居
使用’my_classify.m’函数脚本获得点的类型。
给定参数k，就可以调用这两个函数完成knn 分类器功能。
-Achieve a knn classifier. Using Euclidean distance as a measure of distance using the my_order.m function script to get a point K nearest neighbors use my_classify.m script to get the type of function points. Given the parameters k, we can call these two functions complete knn classification function.
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Size: 13312 |
Author: Tony Shao |
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Description: 这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的胡修改的程序，osu_svm3.00文件夹是使用svm分类器调用的文件，使用该分类器时，代码中间的路径需要修改。另外附上一堆常用的数据集。-This is Hu Qinghua teacher at Tianjin University neighborhood rough set field do the most classic source code, students can learn and modify On this basis, the entry procedures have been written, you need other ways to add your own, MAIN.m entry program is meaning parameters Functions talked in very clear call for the featureselect_FW_fast.m attribute reduction, several clsf_dpd file is to use a different formula to calculate the distance attribute importance, choose properties to get the results, use crossvalidate.m ten fold cross algorithm to calculate the accuracy of the calculation algorithm, the segment code calls several classifiers, C4_5.m is a decision tree, KNN.m is the nearest neighbor classifier, NEC.m is similar to KNN Hu modified program, osu_svm3. 00 folders using svm classifier called file, using the classification code in the middle of the path need to be modified. Also attach a bunch of common data sets.
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Size: 2542592 |
Author: robert |
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Description: KNN algorithm - run main.m file to see result.
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Size: 2048 |
Author: SoheilJahan |
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Description: Validating binary classification with KNN algorithm
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Size: 1024 |
Author: Atma |
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Description: 本实验中的KNN分类算法采用Matlab语言实现。
主函数“knnClass.m”读取训练和测试的数据文件，然后调用knn函数进行分类运算，并使用图像的形式将分类结果显示出来。(The KNN classification algorithm in this experiment is realized by Matlab language.
The main function "knnClass.m" reads the training and test data files, and then calls the knn function for sorting operations and displays the sorting results in the form of images.)
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Size: 43008 |
Author: 岚
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Description: KNN算法,互信息估计，来自于mathworks，经测试可以使用(KNN algorithm, mutual information estimates, from mathworks, tested can be used)
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Size: 1024 |
Author: VicenteN
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