Keywords
multiple statistical prototypes, MSP, learning algorithms
Abstract
Multiple Statistical Prototypes (MSP) is a modification of a standard minimum distance classification scheme that generates muItiple prototypes per class using a modified greedy heuristic. Empirical comparison of MSP with other well-known learning algorithms shows MSP to be a robust algorithm that uses a very simple premise to produce good generalization and achieve parsimonious hypothesis representation.
Original Publication Citation
Ventura, D. and Martinez, T. R., "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pp. 238-245, 1995.
BYU ScholarsArchive Citation
Martinez, Tony R. and Ventura, Dan A., "Using Multiple Statistical Prototypes to Classify Continuously Valued Data" (1995). Faculty Publications. 1160.
https://scholarsarchive.byu.edu/facpub/1160
Document Type
Peer-Reviewed Article
Publication Date
1995-01-01
Permanent URL
http://hdl.lib.byu.edu/1877/2448
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 1995 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/