Algorithms for parallel boosting

We present several algorithms that combine many base learners trained on different distributions of the data, but allow some of the base learners to be trained simultaneously by separate processors. Our algorithms train batches of base classifiers using distributions that can be generated in advance of the training process. We propose several heuristic methods that produce a group of useful distributions based on the performance of the classifiers in the previous batch. We present experimental evidence that suggest that two of our algorithms are able to produce classifiers as accurate as the corresponding Adaboost classifier with the same number of base learners, but with a greatly reduced computation time.