Boosted Mixture of Experts: An Ensemble Learning Scheme

摘要

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are com- bined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting al- gorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner.