COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the exponential value of labeled samples
Pattern Recognition Letters
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Mixture of experts classification using a hierarchical mixture model
Neural Computation
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Active learning with statistical models
Journal of Artificial Intelligence Research
Semi-Supervised Learning
Shared kernel models for class conditional density estimation
IEEE Transactions on Neural Networks
An incremental training method for the probabilistic RBF network
IEEE Transactions on Neural Networks
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
Information Sciences: an International Journal
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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The probabilistic RBF network (PRBF) is a special case of the RBF network and constitutes a generalization of the Gaussian mixture model. In this paper we propose a semi-supervised learning method for PRBF, using labeled and unlabeled observations concurrently, that is based on the expectation-maximization (EM) algorithm. Next we utilize this method in order to implement an incremental active learning method. At each iteration of active learning, we apply the semi-supervised method, and then we employ a criterion to select an unlabeled observation and query its label. This criterion identifies points near the decision boundary. In order to assess the effectiveness of our method, we propose an adaptation of the well-known Query by Committee (QBC) algorithm for the active learning of the PBFR, and present experimental comparisons on several data sets that indicate the effectiveness of the proposed method.