Keywords
machine-learning, stagger, eclectic
Abstract
This project compares two machine-learning methods, Stagger and Eclectic on their classification correctness. Both systems were tested with real-world data sets previously used and tested in other machine learning and statistical literature. The Eclectic System performed better than Stagger on every data set.
Original Publication Citation
Jargalsaihan, B. and Barker, J. C., "A Comparison of Eclectic Learning and Stagger", Proceedings of The International Joint Conference on Neural Networks, Washington, D. C., July 1999.
BYU ScholarsArchive Citation
Barker, J. Cory and Batsaihan, Jargalsaihan, "A Comparison of Eclectic Learning and Stagger" (1999). Faculty Publications. 613.
https://scholarsarchive.byu.edu/facpub/613
Document Type
Peer-Reviewed Article
Publication Date
1999-07-01
Permanent URL
http://hdl.lib.byu.edu/1877/2490
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 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://lib.byu.edu/about/copyright/