Hayy all,, Im going to do classification using SVM.
As I understand we have to project our data into higher dimensional by using kernel. And there are 4 common use kernel (linear, RBF, polynomial, and sigmoid).
- What is the different between those kernel?
- We need to find the linear separable line / hyperplane to classify our data. And to do that, we have to chose large margin to avoid any overfitting? What is overfitting?
- As u can see in this image, there is a w. And in many reference, they said w is a line? What is that actually? And what is that lamda??
- What is SMO mean in SVM?