In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
This paper describes a set of comparative experiments, including cross{corpus evaluation, between ve alternative algorithms for supervised Word Sense Disambiguation (WSD), namely ...
We present a new generalization bound where the use of unlabeled examples results in a better ratio between training-set size and the resulting classifier’s quality and thus red...
Abstract. Oza’s Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the p...
Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, M...
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...