问题
I am still confused with my code. I tried to implement leave one out cross validation in matlab for classification. so in here . I take out one data from training become testing data. I already make a code in matlab. but Iam not sure it's correct because the result is wrong. can someone help me to correct it?? thank you very much.
this is my code :
clc
[C,F] = train('D:\fp\',...
'D:\tp\');
for i=size(F,1)
testVal = i;
trainingSet = setdiff(1:numel(C), testVal); % use the rest for training
Ctrain = C(trainingSet,:);
Ftrain = F(trainingSet,:);
test= F(testVal,:);
svmStruct = svmtrain(Ftrain,Ctrain,'showplot',true,'Kernel_Function','rbf');
result_class(i)= svmclassify(svmStruct,test,'showplot',true);
ax(i)=result_class;
i=i+1;
end
回答1:
This is what I usually use to create leave one out cross-validation.
[Train, Test] = crossvalind('LeaveMOut', N, M)
Here, N will be the number of total samples you have in your training+testing set. M=1 in your case. You can put this in a for loop.
Also, you can use random number generation to perform leave-one out crossvalidation without using predefined function.
来源：https://stackoverflow.com/questions/15451301/how-to-create-leave-one-out-cross-validation-in-matlab