Title :
Adaptive radial basis functions
Author :
Webb, Andrew R. ; Shannon, Simon
Author_Institution :
Defence Res. Agency, Malvern, UK
Abstract :
We develop adaptive radial basis functions: kernel-based models for regression and discrimination where the functional form of the basis function depends on the data. The approach may be regarded as a radial form of projection pursuit, with the additional constraint that the basis functions have a common functional form. We develop the approach for regression and extend it to discrimination via optimal scaling. The motivation behind this study is twofold: (1) the requirement for suitable basis functions for high-dimensional data and (2) to assess optimal scaling as an alternative criterion for training nonlinear models. We assess the approach for regression and discrimination using simulated data
Keywords :
feedforward neural nets; optimisation; pattern recognition; statistical analysis; adaptive radial basis functions; discrimination; kernel-based models; nonlinear models; optimal scaling; projection pursuit; regression; Electronic mail; Kernel; Mars; Mean square error methods; Multilayer perceptrons; Smoothing methods; Spline;
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-8186-7282-X
DOI :
10.1109/ICPR.1996.547641