Keywords
artificial neural network, oracle learning
Abstract
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). Faculty Publications. 1072.
https://scholarsarchive.byu.edu/facpub/1072
Document Type
Peer-Reviewed Article
Publication Date
2002-05-17
Permanent URL
http://hdl.lib.byu.edu/1877/2430
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
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