Keywords
oracle learning, perceptrons, neural networks
Abstract
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). Faculty Publications. 1054.
https://scholarsarchive.byu.edu/facpub/1054
Document Type
Peer-Reviewed Article
Publication Date
2003-05-17
Permanent URL
http://hdl.lib.byu.edu/1877/2439
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
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Copyright Use Information
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