oracle learning, perceptrons, neural networks
Often the best model to solve a real world problem is relatively complex. The following presents oracle learning, a method using a larger model as an oracle to train a smaller model on unlabeled data in order to obtain (1) a simpler acceptable model and (2) improved results over standard training methods on a similarly sized smaller model. In particular, this paper looks at oracle learning as applied to multi-layer perceptrons trained using standard backpropagation. For optical character recognition, oracle learning results in an 11.40% average decrease in error over direct training while maintaining 98.95% of the initial oracle accuracy.
Original Publication Citation
Menke, J., and Martinez, T. R., "Simplifying OCR Neural Networks with Oracle Learning", Proceedings of the IEEE International Workshop on Soft-Computing Techniques in Instrumentation, Measurement, and Related Applications, pp. 6-13, 23.
BYU ScholarsArchive Citation
Martinez, Tony R. and Menke, Joshua, "Simplifying OCR Neural Networks with Oracle Learning" (2003). All Faculty Publications. 1054.
Physical and Mathematical Sciences
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