Svm Using Matlab
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TRAIN GRAPH
TEST GRAPH
CLASSIFICATION PROGRAM >> %Classification Program. >> >> %Load the data, which includes proximates analysis data of 60 samples. >> load project >> data=[proximate(:,1),proximate(:,4)]; %Create data two column matrix.
>> groups=ismember(label,'biomass'); %From the label vector, groups, to classify data into two classes: Biomass and Non-biomass. >> [train,test]=crossvalind('holdOut',groups,0.5); %Split train and test. >> cp=classperf(groups); %Indicate performance of classifier. >> >> %svmtrain function to train svm classifier using a RBF Kernel and plot the grouped data. >> svmStruct=svmtrain(data(train,:),groups(train),'Kernel_Fu nction','rbf','RBF_Sigma',0.5,'boxconstraint',0.03125,'show plot',true); >> title(sprintf('Kernel Function: %s',... func2str(svmStruct.KernelFunction)),... 'interpreter','none'); >> classes=svmclassify(svmStruct,data(test,:),'showplot',true ); %Classify the test set using SVM. >> classperf(cp,classes,test); %Evaluate the performance of classifier.
>> cp.correctRate
ans =
0.9000
TEST GRAPH
CLASSIFICATION PROGRAM >> %Classification Program. >> >> %Load the data, which includes proximates analysis data of 60 samples. >> load project >> data=[proximate(:,1),proximate(:,4)]; %Create data two column matrix.
>> groups=ismember(label,'biomass'); %From the label vector, groups, to classify data into two classes: Biomass and Non-biomass. >> [train,test]=crossvalind('holdOut',groups,0.5); %Split train and test. >> cp=classperf(groups); %Indicate performance of classifier. >> >> %svmtrain function to train svm classifier using a RBF Kernel and plot the grouped data. >> svmStruct=svmtrain(data(train,:),groups(train),'Kernel_Fu nction','rbf','RBF_Sigma',0.5,'boxconstraint',0.03125,'show plot',true); >> title(sprintf('Kernel Function: %s',... func2str(svmStruct.KernelFunction)),... 'interpreter','none'); >> classes=svmclassify(svmStruct,data(test,:),'showplot',true ); %Classify the test set using SVM. >> classperf(cp,classes,test); %Evaluate the performance of classifier.
>> cp.correctRate
ans =
0.9000
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