The linear kernel is defined as: $K(x1,x2)=\langle x1,x2\rangle$. I can see that all that this kernel does is to calculate the dot product in the original space of the data. Why is this kernel then needed? Does it add anything to the plain (kernel-less) SVM?
I fail to understand this kernel; someone help clarify on why there is such a thing as a linear kernel. what does it add to the plain SVM implementation.