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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
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Size: 235527 |
Author: hehuixin |
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Description: 脱机手写体识别Matlab源程序
包括特征提取、bayes分类器、K近邻分类及最近邻分类。
TestScriptRecognition.m：测试代码
ScriptFeaExtract.m ：特征提取
KNearestEstimate.m ：K近邻估计
NearestEstimate.m : 最近邻估计
BayesTrain.m ：训练bayes分类器
Bayes.m ：测试bayes分类器
CrossValidate.m ：m交叉验证
-Offline Handwriting Recognition Matlab source including feature extraction, bayes classifier, K-nearest neighbor and nearest neighbor classifier. TestScriptRecognition.m : ScriptFeaExtract.m test code : Feature Extraction KNearestEstimate.m : K-nearest neighbor is estimated NearestEstimate.m : nearest neighbor is estimated BayesTrain.m : Training bayes classifier Bayes.m : Test bayes classifier CrossValidate.m : m cross-certification
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Size: 6683 |
Author: KOF |
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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 |
Hits:
Description: 脱机手写体识别Matlab源程序
包括特征提取、bayes分类器、K近邻分类及最近邻分类。
TestScriptRecognition.m：测试代码
ScriptFeaExtract.m ：特征提取
KNearestEstimate.m ：K近邻估计
NearestEstimate.m : 最近邻估计
BayesTrain.m ：训练bayes分类器
Bayes.m ：测试bayes分类器
CrossValidate.m ：m交叉验证
-Offline Handwriting Recognition Matlab source including feature extraction, bayes classifier, K-nearest neighbor and nearest neighbor classifier. TestScriptRecognition.m : ScriptFeaExtract.m test code : Feature Extraction KNearestEstimate.m : K-nearest neighbor is estimated NearestEstimate.m : nearest neighbor is estimated BayesTrain.m : Training bayes classifier Bayes.m : Test bayes classifier CrossValidate.m : m cross-certification
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Size: 6144 |
Author: |
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Description: 十折交叉验证方法的matlab code 再模式识别中是一个非常重要的试验方法-a very important technique crossvalidate in pattern recognazition
Platform: |
Size: 1024 |
Author: zhupengfei |
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Description: 一个交叉验证的matlab源码，语句简单，清楚，易修改，好实现-Matlab source code, a cross-validation statement is simple, clear, easy to modify, good to achieve
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Size: 1024 |
Author: zhanglingjun |
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Description: 10折交叉验证代码 自编函数 可用于任何数据 直接使用即可-10 fold cross validation code editing the function can be used in any data can be used directly
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Size: 1024 |
Author: 王 |
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Description: 神经网络的K折交叉验证用于模型选择和测试数据验证-crossvalidate of ANN
Platform: |
Size: 7168 |
Author: 邱 |
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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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