Keywords
radial basis function networks, distance function
Abstract
Radial Basis Function (RBF) networks typically use a distance function designed for numeric attributes, such as Euclidean or city-block distance. This paper presents a heterogeneous distance function which is appropriate for applications with symbolic attributes, numeric attributes, or both. Empirical results on 30 data sets indicate that the heterogeneous distance metric yields significantly improved generalization accuracy over Euclidean distance in most cases involving symbolic attributes.
Original Publication Citation
Wilson, D. R. and Martinez, T. R., "Heterogeneous Radial Basis Function Networks", Proceedings of the ICNN'96 IEEE International Conference on Neural Networks, pp. 263-1267, 1996.
BYU ScholarsArchive Citation
Martinez, Tony R. and Wilson, D. Randall, "Heterogeneous Radial Basis Function Networks" (1996). Faculty Publications. 1148.
https://scholarsarchive.byu.edu/facpub/1148
Document Type
Peer-Reviewed Article
Publication Date
1996-06-01
Permanent URL
http://hdl.lib.byu.edu/1877/2424
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 1996 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/