Margins, Shrinkage, and Boosting


Matus Telgarsky ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):307-315, 2013.


This manuscript shows that AdaBoost and its immediate variants can produce approximately maximum margin classifiers simply by scaling their step size choices by 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.

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