Active Learning with the Probabilistic RBF Classifier
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.
KeywordsActive Learning Gaussian Mixture Model Decision Boundary Unlabeled Data Active Learning Method
Unable to display preview. Download preview PDF.
- 5.McCallum, A.K., Nigam, K.: Employing EM in pool-based active learning for text classification. In: Shavlik, J.W. (ed.) Proc. 15th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1998)Google Scholar
- 7.Zhu, X., Lafferty, J., Ghahramani, Z.: Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In: Proc. 20th International Conference on Machine Learning (2003)Google Scholar
- 8.Ghahramani, Z., Jordan, M.: Supervised learning from incomplete data via an EM approach. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems 6. Morgan Kaufmann, San Francisco (1994)Google Scholar
- 13.Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
- 14.Constantinopoulos, C., Likas, A.: An incremental training method for the probabilistic RBF network. IEEE Trans. Neural Networks (2006) (to appear)Google Scholar