Keywords
connectionist networks, incremental rule learning, example learning, generalization
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.
Original Publication Citation
Martinez, T. R., "Consistency and Generalization of Incrementally Trained Connectionist Models", Proceedings of the International Symposium on Circuits and Systems, pp. 76-79, 199.
BYU ScholarsArchive Citation
Martinez, Tony R., "Consistency and Generalization in Incrementally Trained Connectionist Networks" (1990). Faculty Publications. 1189.
https://scholarsarchive.byu.edu/facpub/1189
Document Type
Peer-Reviewed Article
Publication Date
1990-05-03
Permanent URL
http://hdl.lib.byu.edu/1877/2417
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 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://lib.byu.edu/about/copyright/