LEARN++: an incremental learning algorithm for multilayer perceptron networks

We introduce a supervised learning algorithm that gives neural network classification algorithms the capability of learning incrementally from new data without forgetting what has been learned in earlier training sessions. Schapire´s (1990) boosting algorithm, originally intended for improving the accuracy of weak learners, has been modified to be used in an incremental learning setting. The algorithm is based on generating a number of hypotheses using different distributions of the training data and combining these hypotheses using a weighted majority voting. This scheme allows the classifier previously trained with a training database, to learn from new data when the original data is no longer available, even when new classes are introduced. Initial results on incremental training of multilayer perceptron networks on synthetic as well as real-world data are presented in this paper