COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Information-based objective functions for active data selection
Neural Computation
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Neural Network Exploration Using Optional Experiment Design
Neural Network Exploration Using Optional Experiment Design
A Formulation for Active Learning with Applications to Object Detection
A Formulation for Active Learning with Applications to Object Detection
Active learning with statistical models
Journal of Artificial Intelligence Research
Evolving model trees for mining data sets with continuous-valued classes
Expert Systems with Applications: An International Journal
Auto-adaptive and dynamical clustering neural network
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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Active learning algorithms allow neural networks to dynamically take part in the selection of the most informative training patterns. This paper introduces a new approach to active learning, which combines an unsupervised clustering of training data with a pattern selection approach based on sensitivity analysis. Training data is clustered into groups of similar patterns based on Euclidean distance, and the most informative pattern from each cluster is selected for training using the sensitivity analysis incremental learning algorithm in (Engelbrecht and Cloete, 1999). Experimental results show that the clustering approach improves on standard active learning as presented in (Engelbrecht and Cloete, 1999).