Abstract: The present invention provides a method and device for predicting the target class of a set of examples using a sequence of inductive learning hypotheses. The invention starts by having a set of training examples. The output to each training example is one of the target classes. An inductive learning algorithm is trained on the set of training examples. The resulting trained hypothesis then predicts the target class for many examples. A user, with the help of a computer-human interface, accepts the predictions or corrects a subset of them. Two methods are used to process the correction. The first is to combine the corrections with the training set, create a new hypothesis by training a learning algorithm, and replacing the last hypothesis in the sequence with the newly trained hypothesis.