I have a dataset composed of some target and non target. I have used Support Vector Machine to classify. I am taking 50% data for training and testing all data.
If I use linear kernel, I get very good classification result. But if I use RBF kernel my resuls are not good. The question is, can we get good result for linear kernel and bad result for RBF kernel for the same dataset?
I was under impression that RBF kernel will also work for linearly seperably data? Can anyone please clarify? I am not ging the details of my work as I assumed it is not relevant to the question, and asking a general point of view?