We apply the same online large-margin training principle; however, the parameter update has to be adjusted for the scaled linear model.
the new goal is to find a separation betweenf (e + ) andf (e − ) instead of f (e + ) and f (e − ), for which the optimization corresponding to the parameter update becomes: where ⃗ θ 0 and ⃗ θ represent the parameter vectors before and after the update, respectively.