  This manuscript shows that AdaBoost and its immediate variants can produce
approximate maximum margin classifiers simply by scaling step size choices with
a fixed small constant. In this way, when the unscaled step size is an optimal
choice, these results provide guarantees for Friedman's empirically successful
"shrinkage" procedure for gradient boosting (Friedman, 2000). Guarantees are
also provided for a variety of other step sizes, affirming the intuition that
increasingly regularized line searches provide improved margin guarantees. The
results hold for the exponential loss and similar losses, most notably the
logistic loss.
