Summary

SVM's find the optimal decision boundary (defined by support vectors)
Non-linear boundaries are treated by working in a higher dimensional space
Data doesn't need to be transformed to the higher dim space if we make use of a suitable kernel

SVM's find the optimal decision boundary (defined by support vectors)