I am experimenting with support vector machines (SVM) following this book
Without a kernel, it is very easy to "summarize" the SVM optimal solution, as you only need the separator hyperplane w, equal in dimensionality to the number of features considered
when using a kernel on the other hand (e.g. RBF), the real separation is in a higher dimensionality space, so to classify an unseen sample you don't have a simple hyperplane; instead you need to store all the support vectors (part of the training set) and their langrangean multipliers, which is substantially more "complicated".
In the book mentioned above there are some plots that map the separating plane down to the original low dimensionality feature space, like this
I wonder how the author came up with this curve? Is there any procedure for mapping the solution to the 2D space or did he just sample the separating function to draw up the boundary "manually"?