A Bayesian Boosting Model
Machine Learning
2012-09-11 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1209.1996,
title = {A Bayesian Boosting Model},
author = {Alexander Lorbert and David M. Blei and Robert E. Schapire and Peter J. Ramadge},
journal= {arXiv preprint arXiv:1209.1996},
year = {2012}
}