radial basis function networks, distance function
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). All Faculty Publications. 1148.
Physical and Mathematical Sciences
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