text-classification
所属分类:数值算法/人工智能
开发工具:matlab
文件大小:3441KB
下载次数:4
上传日期:2017-11-30 11:02:46
上 传 者:
bx425bob
说明: 文本分类程序,很多的算法集合,还有各个算法的结果比较
(Text classification procedures, the set of many algorithms and compare the results of the algorithm)
文件列表:
text-classification\3NN.fig (12666, 2012-05-10)
text-classification\cv.txt (31507, 2012-05-10)
text-classification\datafilelist.txt (412, 2012-04-25)
text-classification\KNN.fig (14452, 2012-05-10)
text-classification\KNN_test.m (1070, 2012-05-04)
text-classification\KNN_validation.m (2063, 2012-05-09)
text-classification\nb.fig (15732, 2012-05-04)
text-classification\nb_accuracy.mat (315, 2012-05-03)
text-classification\nb_test.m (1024, 2012-05-11)
text-classification\nb_train.m (1422, 2017-09-18)
text-classification\nb_validation.m (1705, 2012-05-02)
text-classification\rewrite_os-4-1-f1000.dat (4006306, 2012-04-24)
text-classification\rewrite_os-4-1-f2000.dat (8006400, 2012-04-24)
text-classification\SVM.fig (11760, 2012-05-09)
text-classification\tfidf.m (1287, 2012-04-24)
text-classification\文本分类.doc (221696, 2012-05-10)
text-classification\文本分类.ppt (545280, 2012-05-16)
text-classification\tfidf程序\tfidf.m (2233, 2016-11-13)
text-classification\SVM_TextClass\a_template_flow_usingSVM_class.m (2519, 2012-04-27)
text-classification\SVM_TextClass\a_template_flow_usingSVM_regress.m (2338, 2012-04-27)
text-classification\SVM_TextClass\ClassResult.m (2086, 2012-04-27)
text-classification\SVM_TextClass\ClassResult_test.m (366, 2012-04-27)
text-classification\SVM_TextClass\datafilelist.txt (412, 2012-04-25)
text-classification\SVM_TextClass\gaSVMcgForClass.m (3579, 2012-04-27)
text-classification\SVM_TextClass\gaSVMcgForRegress.m (3463, 2012-04-27)
text-classification\SVM_TextClass\gaSVMcgpForRegress.m (3744, 2012-04-27)
text-classification\SVM_TextClass\heart_scale.mat (28904, 2005-03-22)
text-classification\SVM_TextClass\libsvmread.c (3988, 2011-02-24)
text-classification\SVM_TextClass\libsvmread.mexw32 (20480, 2011-06-08)
text-classification\SVM_TextClass\libsvmwrite.c (2123, 2011-02-24)
text-classification\SVM_TextClass\libsvmwrite.mexw32 (20480, 2011-06-08)
text-classification\SVM_TextClass\libsvm参数说明.txt (2865, 2012-04-27)
text-classification\SVM_TextClass\make.m (396, 2011-02-24)
text-classification\SVM_TextClass\Makefile (1481, 2011-02-24)
text-classification\SVM_TextClass\pcaForSVM.m (1272, 2012-04-27)
text-classification\SVM_TextClass\plotSVMroc.m (1304, 2012-04-27)
text-classification\SVM_TextClass\plotSVMroc_test.m (381, 2012-04-27)
text-classification\SVM_TextClass\plotSVMroc_test2.m (1002, 2012-04-27)
text-classification\SVM_TextClass\psoSVMcgForClass.m (5586, 2012-04-27)
text-classification\SVM_TextClass\psoSVMcgForRegress.m (5349, 2012-04-27)
... ...
SVM faruto version
by faruto
Email:farutoliyang@gmail.com
2009.11.05
==================================
Content:
scaleForSVM:归一化
函数接口:
[train_scale,test_scale,ps] = scaleForSVM(train_data,test_data,ymin,ymax)
====================================
pcaForSVM:pca降维预处理
函数接口:
[train_pca,test_pca] = pcaForSVM(train,test,threshold)
====================================
fasticaForSVM:ica降维预处理
函数接口:
[train_ica,test_ica] = fasticaForSVM(train,test)
====================================
SVMcgForClass:分类问题参数寻找[grid search based on CV]
函数接口:
[bestacc,bestc,bestg] = SVMcgForClass(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,accstep)
SVMcgForRegress:回归问题参数寻优[grid search based on CV]
函数接口:
[mse,bestc,bestg] = SVMcgForRegress(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,msestep)
======================================
psoSVMcgForClass:分类问题参数寻优[pso based on CV]
函数接口:
[bestCVaccuracy,bestc,bestg,pso_option] = psoSVMcgForClass(train_label,train,pso_option)
psoSVMcgForRegress:回归问题参数寻优[pso based on CV]
函数接口:
[bestCVmse,bestc,bestg,pso_option] = psoSVMcgForRegress(train_label,train,pso_option)
=======================================
gaSVMcgForClass:分类问题参数寻优[ga based on CV]
函数接口:
[bestCVaccuracy,bestc,bestg,ga_option] = gaSVMcgForClass(train_label,train,ga_option)
gaSVMcgForRegress:回归问题参数寻优[ga based on CV]
函数接口:
[bestCVmse,bestc,bestg,ga_option] = gaSVMcgForRegress(train_label,train,ga_option)
======================================
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