Active learning with the probabilistic RBF classifier

Abstract

In this work we present an active learning methodology for training the probabilistic RBF (PRBF) network. It is a special case of the RBF network, and constitutes a generalization of the Ganssian mixture model. We propose an incremental method for semi-supervised learning based on the Expectation-Maximization (EM) algorithm. Then we present an active learning method that iteratively applies the semi-supervised method for learning the labeled and unlabeled observations concurrently, and then employs a suitable criterion to select an unlabeled observation and query its label. The proposed criterion selects points near the decision boundary, and facilitates the incremental semi-supervised learning that also exploits the decision boundary. The performance of the algorithm in experiments using well-known data sets is promising. © Springer-Verlag Berlin Heidelberg 2006.