Incorporating Prior Knowledge into Boosting
We describe a modi cation to the AdaBoost algorithm that permits the incorporation of prior human knowledge as a means of compensating for a shortage of training data. We give a convergence result for the algorithm, and we describe experiments on four datasets showing that prior knowledge can substantially improve performance.