I'm using libsvm in Matlab to classify a dataset with 5 classes. Data here is 2-D but I don't think it matters.
The amount of testing data for each class is balanced. For training data, when I use 5 training sample for each class, the classification result is good. However, when I change the number of training data for a class (say class 2) from 5 to 10, the classification accuracy is poor, especially for class 2.
The code I use is very simple:
model = svmtrain2(trainLabels, trainData );
[LabelSVM] = svmpredict2(testLabels, testData, model);
Is that because there's any options in
svmtrain2 that I should specify? Or it's caused by something else? Thank you.