Active learning with the probabilistic RBF classifier

Authors:
Constantinos Constantinopoulos;Aristidis Likas
Affiliations:
Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 11
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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 Gaussian 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.