Collaborative learning from multiple data sources

A machine learning classifier can be trained on an labeled input data set, which comprise samples and their corresponding labels, to predict the labels of samples that it has never seen before. The problem of combining several machine learning classifiers to achieve a result that is greater than the sum of its parts has been studied under the guise of collaborative learning. The vast majority of collaborative learning methods simply train several base classifiers and take a majority vote on the predictions made by each base classifier.