A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic princip...
In this paper, we present a new interpretation of AdaBoost.ECC and AdaBoost.OC. We show that AdaBoost.ECC performs stage-wise functional gradient descent on a cost function, defin...
We give a review of various aspects of boosting, clarifying the issues through a few simple results, and relate our work and that of others to the minimax paradigm of statistics. ...
Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...