We consider the problem of learning a forest of nonlinear decision rules with
general loss functions. The standard methods employ boosted decision trees such
as Adaboost for exponential loss and Friedman's gradient boosting for general
In contrast to these traditional boosting algorithms that treat a tree
learner as a black box, the method we propose directly learns decision forests
via fully-corrective regularized greedy search using the underlying forest
structure. Our method achieves higher accuracy and smaller models than gradient
boosting (and Adaboost with exponential loss) on many datasets.