artifical neural network, oracle learning
Often the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler models with comparable accuracy. The following research presents evidence that using a larger model as an oracle to train a smaller model on unlabeled data results in 1) a simpler acceptable model and 2) improved results over standard training methods on a similarly sized smaller model. On automated spoken digit recognition, oracle learning resulted in an artificial neural network of half the size that 1) maintained comparable accuracy to the larger neural network, and 2) obtained up to a 25% decrease in error over standard training methods.
Original Publication Citation
Menke, J., Peterson, A., Rimer, M, and Martinez, T. R., "Network Simplification through Oracle Learning", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'2, pp. 2482-2497, 22.
BYU ScholarsArchive Citation
Martinez, Tony R.; Menke, Joshua; Peterson, Adam; and Rimer, Michael E., "Network Simplification Through Oracle Learning" (2002). All Faculty Publications. 1072.
Physical and Mathematical Sciences
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