Ensemble learning or model ensembling, is a well-established set of machine learning and statistical techniques for improving predictive performance through the combination of different learning algorithms. The combination of the predictions from different models is generally more accurate than any of the individual models making up the ensemble. Ensemble methods come in different flavours and levels of complexity (for a review see https://arxiv.org/pdf/1106.0257.pdf), but here we focus on combining the predictions of multiple deep learning networks that have been previously trained.

Full article at https://blogs.mathworks.com/deep-learning/2019/06/03/ensemble-learning/