SVMs take a simple strategy that finds the separating hyperplane which maximizes its margin.
two dashed lines and margin (d) can be written as: SVMs can be regarded as an optimization problem; finding w and b which minimize [[w[[ under the constraints: yi Furthermore, SVMs have potential to cope with the linearly unseparable training data.