Consistency and Generalization in Incrementally Trained Connectionist Networks

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Permanent URL	http://hdl.lib.byu.edu/1877/2417
Title	Consistency and Generalization in Incrementally Trained Connectionist Networks
BYU Creator	Martinez, Tony R.
Keywords	connectionist networks; incremental rule learning; example learning; generalization
Description/Abstract	This paper discusses aspects of consistency and generalization in connectionist networks which learn through incremental training by examples or rules. Differences between training set learning and incremental rule or example learning are presented. Generalization, the ability to output reasonable mappings when presented with novel input patterns, is discussed in light of the above learning methods. In particular, the contrast between humming distance generalization and generalizing by high order combinations of critical variables is overviewed. Examples of detailed rules for an incremental learning model are presented for both consistency and generalization constraints.
Source	Martinez, T. R., "Consistency and Generalization of Incrementally Trained Connectionist Models", Proceedings of the International Symposium on Circuits and Systems, pp. 706-709, 1990.;
Publisher	IEEE
Date Original	1990
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) 1990 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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Consistency and Generalization in Incrementally Trained Connectionist Networks

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