Title :
Shape-adaptive radial basis functions
Author :
Webb, Andrew R. ; Shannon, Simon
Author_Institution :
Defence Evaluation & Res. Agency, UK
fDate :
11/1/1998 12:00:00 AM
Abstract :
Radial basis functions for discrimination and regression have been used with some success in a wide variety of applications. Here, we investigate the optimal choice for the form of the basis functions and present an iterative strategy for obtaining the function in a regression context using a conjugate gradient-based algorithm together with a nonparametric smoother. This is developed in a discrimination framework using the concept of optimal scaling. Results are presented for a range of simulated and real data sets
Keywords :
conjugate gradient methods; iterative methods; optimisation; pattern recognition; radial basis function networks; statistical analysis; conjugate gradient method; discriminant analysis; iterative method; neural nets; nonlinear optimisation; nonlinear transformation; nonparametric regression; optimal scaling; shape-adaptive radial basis functions; statistical pattern recognition; Additive noise; Data analysis; Gradient methods; Iterative algorithms; Kernel; Least squares approximation; Mean square error methods; Multilayer perceptrons; Pattern analysis; Smoothing methods;
Journal_Title :
Neural Networks, IEEE Transactions on
