Keywords
pair attribute, learning algorithm
Abstract
We present the Pair Attribute Learning (PAL) algorithm for the selection of relevant inputs and network topology. Correlations on training instance pairs are used to drive network construction of a single-hidden layer MLP. Results on nine learning problems demonstrate 70% less complexity, on average, without a significant loss of accuracy.
Original Publication Citation
Henderson, E., and Martinez, T. R., "Pair Attribute Learning: Network Construction Using Pair Features", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'2, pp. 2556-2561, 22.
BYU ScholarsArchive Citation
Martinez, Tony R. and Henderson, Eric K., "Pair Attribute Learning: Network Construction Using Pair Features" (2002). Faculty Publications. 1074.
https://scholarsarchive.byu.edu/facpub/1074
Document Type
Peer-Reviewed Article
Publication Date
2002-01-01
Permanent URL
http://hdl.lib.byu.edu/1877/2434
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
http://lib.byu.edu/about/copyright/