The Consistency of Greedy Algorithms for Classification
We consider a class of algorithms for classification, which are based on sequential greedy minimization of a convex upper bound on the 0 - 1 loss function. A large class of recently popular algorithms falls within the scope of this approach, including many variants of Boosting algorithms. The basic question addressed in this paper relates to the statistical consistency of such approaches. We provide precise conditions which guarantee that sequential greedy procedures are consistent, and establish rates of convergence under the assumption that the Bayes decision boundary belongs to a certain class of smooth functions. The results are established using a form of regularization which constrains the search space at each iteration of the algorithm. In addition to providing general consistency results, we provide rates of convergence for smooth decision boundaries. A particularly interesting conclusion of our work is that Logistic function based Boosting provides faster rates of convergence than Boosting based on the exponential function used in AdaBoost.