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The Little Neuron that Could

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Permanent URL	http://hdl.lib.byu.edu/1877/2444
Title	The Little Neuron that Could
BYU Creator	Andersen, Timothy L. Martinez, Tony R.
Keywords	perceptrons; generalization; SLP; weight averaging
Description/Abstract	SLPs (single layer perceptrons) oflen exhibit reasonable generalization performance on many problems of interest. However, due to the well known limitations of SLPs very little effort has been made to improve their performance. This paper proposes a method for improving the performance of SLPs called "wagging" (weight averaging). This method involves training several different SLPs on the same training data, and then averaging their weights to obtain a single SLP. The performance of the wagged SLP is compared with other more complex learning algorithms (bp, c4.5, ibl, MML, etc) on 15 data sets from real world problem domains. Surprisingly, the wagged SLP has better average generalization performance than any of the other learning algorithms on the problems tested. This result is explained and analyzed. The analysis includes looking at the performance characteristics of the standard delta rule training algorithm for SLPs and the correlation between training and test set scores as training progresses.
Source	Andersen, T. L. and Martinez, T. R., "The Little Neuron that Could", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99, CD paper #191, 1999.;
Publisher	IEEE
Date Original	1999
Date Digital	2010-09-09
Language	English eng en
Publication Type	Peer-Reviewed Articles Conference Proceedings
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 1999 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://www.lib.byu.edu/generic_copyright.html

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