ssion to linear SVMs is just a matter of changing the loss
from the log loss to the hinge loss. The hinge-loss is defined as ...
And we can penalize using either l1 or l2 norm, or again, in principle also elastic net.
This formulation with the hinge loss doesn't really make sense without the penalty,
because of the formulation of the hinge loss.
What this loss says is basically "if you predict the right class with a margin of 1, there is no loss".
Otherwise the loss is linear in the decision function. So yo