Kernel Method Note
Motivation of Kernel Method In classifications, it is often the case that we want to obtain a non-linear decision boundary. For example, for this problem (figure 2), we want a desicion boundary that is somewhat like a circle, however, our model only yields linear boundaries. In order to let our model to have more flexibility without changing the basic algorithms, we can apply a transformation onto the feature space $X$. like the figure on the right....