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資源簡介

基于半監督的svm的圖像分類方法。通過聚類核的方法利用無標記樣本局部正則化訓練核的表達式。這種方法通過圖像直接學習一個自適應的核。程序仿真的文章是:Semi-supervised Remote Sensing Image Classification

資源截圖

代碼片段和文件信息

%?Matlab?demo?for?Bagged?Support?Vector?Machines?(BAG?SVM)
%?http://www.uv.es/gcamps/bagsvm/

%?Paper:????“Semi-supervised?Remote?Sensing?Image?Classification?with?Cluster?Kernels“
%???????????Devis?Tuia?and?Gustavo?Camps-Valls
%???????????IEEE?Geoscience?and?Remote?Sensing?Letters?2008?submitte


%?Inputs:???-?train?=?vector?(m?x?p?+?1)?containing?
%????????????????????p?features?of?the?m?labeled?data?(m?x?p)??
%????????????????????1?label?vector?(m?x?1)
%???????????-?testX?=?vector?(n?x?p?+?1)?containing?
%????????????????????p?features?of?the?n?unlabeled?data?(n?x?p)??
%????????????????????1?label?vector?(n?x?1)???????????
%???????????-?mode?=?‘p‘?(product)?or?‘s‘?(sum).?Default?=?‘s‘

%?Outputs:??-?accSVM?=?accuracy?of?the?standard?SVM
%???????????-?accBAG?=?accuracy?of?the?BAG?SVM

%?requires?LibSVM?(http://gpds.uv.es/~jordi/libsvm/)


%?Devis?Tuia?(devis.tuia@unil.ch)?and?Gustavo?Camps-Valls?(gcamps@uv.es)?2008

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function?[accSVM?accBAG]?=?demo_BAG_SVM(traintestNkmode)

close?all
addpath(‘./code_svm‘)



if?exist(‘mode‘‘var‘)?==?0
????mode?=?[‘s‘];
????disp(‘No?mode?selected?sum?kernel?by?default.‘)
else
????if?(mode?~=?[‘s‘])
????????if(mode?~=?[‘p‘])
????????????mode?=?[‘s‘];
????????????disp(‘No?mode?selected?sum?kernel?by?default.‘)
????????end
????end
end



%-----------------------------------------
%data?prep?(if?not?used?as?a?function)

%?train?=?textread(‘labeled.txt‘);
%?test?=?textread(‘unlabeled.txt‘);
%?
%?%----
%?%good?results?are?obtained?with:
%?k?=?5
%?N?=?50
%?mode?=?[‘s‘]

%?k?=?2
%?N?=?50
%mode?=?[‘p‘]
%----

Xtrain?=?train(:1:2);
Ytrain?=?train(:3);
Xtest?=?test(:1:2);
Ytest?=?test(:3);
scatter(test(:1)test(:2)30test(:3))
hold?on
scatter(train(:1)train(:2)303*train(:3)‘filled‘)
title(‘Initial?dataset‘)

[tempidtr]=sortrows(Ytrain);
[tempidts]=sortrows(Ytest);clear?temp

%-----------------------------------------
%Standard?SVM

disp(‘Full?SVM‘)
j=0;
trainings?=?10
for?ss?=?logspace(-33trainings)
????Ktrain?=?kernelmatrix(‘rbf‘Xtrain‘Xtrain‘ss);
????
????for?cc?=?logspace(-33trainings)
????????j=j+1;
????????model??=?svmtrain(YtrainKtrain[‘-t?4?-v?3?-c?‘?num2str(cc)]);
????????RES_SVM(j:)?=?[ss?cc?model];
????end;
end;

%?Select?the?best?model
[kk?j]?=?max(RES_SVM(:3));
sigma?=?RES_SVM(j1);
C?=?RES_SVM(j2);
clear?trainings?j?ss?cc

%?Train?with?the?best?model
Ktrain?=?kernelmatrix(‘rbf‘Xtrain‘Xtrain‘sigma);
Ktest??=?kernelmatrix(‘rbf‘Xtrain‘Xtest‘sigma);
model??=?svmtrain(YtrainKtrain[‘-t?4?-c?‘?num2str(C)]);

%?Predict?in?test
[YpredaccSVM]?=?svmpredict(YtestKtest‘model);
figure
scatter(test(:1)test(:2)30Ypred‘filled‘)
hold?on
scatter(train(:1)train(:2)303*train(:3)‘filled‘)
title(‘Standard?SVM‘)
%ACCURACY_FULL?=??assessment(YtestYpred‘class‘);

%-----------------------------------------
%Construct?BagSVM

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2008-09-01?15:39??demoBagSVM\
?????文件????????6363??2008-09-01?09:05??demoBagSVM\BagSVM.m
?????文件????????3436??2008-09-01?15:39??demoBagSVM\README
?????文件?????????704??2008-09-01?15:28??demoBagSVM\demo.m
?????文件????????1629??2008-07-08?18:26??demoBagSVM\build_Kbag.m
?????文件????????1191??2008-05-23?12:42??demoBagSVM\kernelmatrix.m
?????文件?????????387??2008-06-02?09:06??demoBagSVM\closerCluster.m
?????目錄???????????0??2008-09-01?15:06??demoBagSVM\code_svm\
?????文件???????68500??2008-05-23?12:42??demoBagSVM\code_svm\svmtrain.mexglx
?????文件???????28672??2008-05-26?09:14??demoBagSVM\code_svm\svmpredict.dll
?????文件???????64561??2008-05-23?12:42??demoBagSVM\code_svm\svmpredict.mexglx
?????文件???????49152??2008-05-26?09:14??demoBagSVM\code_svm\svmtrain.dll
?????文件????????6148??2008-08-30?18:23??demoBagSVM\code_svm\.DS_Store
?????文件????????9117??2008-08-30?18:08??demoBagSVM\unlabeled.txt
?????文件?????????549??2008-08-30?18:55??demoBagSVM\labeled.txt
?????文件????????2067??2007-10-05?15:54??demoBagSVM\L2_distance.m

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