Figure 2.

Kernel-based mapping from a lower dimensional space (X) to a higher dimensional space (Z) called the feature space, where non-separable classes become linearly separable. Already established linear theory could be used to estimate the separating hyperplane. Samples on the margin are denoted as support vectors and they define the prediction function, which could be implemented efficiently using the kernel trick.