A Bayesian Boosting Model
Alexander Lorbert, David M. Blei, Robert E. Schapire, Peter J. Ramadge · arXiv (Cornell University) · 2012
We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections to AdaBoost and give experimental results from four datasets.