Boosting algorithms have been shown to perform well on many realworld problems, although they sometimes tend to overfit in noisy situations. While excellent finite sample bounds are known, it has not been clear whether boosting is statistically consistent, implying asymptotic convergence to the optimal classification rule. Recent work has provided sufficient conditions for the consistency of bo...

It will be most efficient to frame operation strategies before actual taxi demand is revealed. But this is challenging due to limited knowledge of the taxi demand distribution in immediate future and is more prone to prediction errors. In this study, we develop the boosting Gaussian conditional random field (boosting-GCRF) model to accurately forecast the short-term taxi demand distribution usi...

In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer chur...

RegionBoost is one of the classical examples of Boosting with dynamic weighting schemes. Apart from its demonstrated superior performance on a variety of classification problems, relatively little effort has been devoted to the detailed analysis of its convergence behavior. This paper presents some results from a preliminary attempt towards understanding the practical convergence behavior of Re...

In the past ten years, boosting has become a major field of machine learning and classification. This paper brings contributions to its theory and algorithms. We first unify a well-known top-down decision tree induction algorithm due to [Kearns and Mansour, 1999], and discrete AdaBoost [Freund and Schapire, 1997], as two versions of a same higher-level boosting algorithm. It may be used as the ...

We thank the authors for writing a thought-provoking piece that may ruffle the feathers of recent orthodoxies in boosting. We also thank JMLR for publishing this article! Since the late 1990s, boosting has undergone the equivalent of a simultaneous X-ray, fMRI and PET exam, and the common view these days is that boosting is a kind of model fitting. As such, it is subjected to assumptions that a...

Branching programs are a generalization of decision trees. From the viewpoint of boosting theory the former appear to be exponentially more eecient. However, earlier experience demonstrates that such results do not necessarily translate to practical success. In this paper we develop a practical version of Mansour and McAllester's 13] algorithm for branching program boosting. We test the algorit...