Adaptive radial basis functions
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4 (10514651) , 630-634 vol.4
- https://doi.org/10.1109/icpr.1996.547641
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
This publication has 10 references indexed in Scilit:
- On the use of nonlocal and non positive definite basis functions in radial basis function networksPublished by Institution of Engineering and Technology (IET) ,1995
- Flexible Discriminant Analysis by Optimal ScoringJournal of the American Statistical Association, 1994
- Functional approximation by feed-forward networks: a least-squares approach to generalizationIEEE Transactions on Neural Networks, 1994
- Nonparametric Regression and Generalized Linear ModelsPublished by Springer Nature ,1994
- Multivariate Adaptive Regression SplinesThe Annals of Statistics, 1991
- Probabilistic neural networksNeural Networks, 1990
- Projection PursuitThe Annals of Statistics, 1985
- Discussion: Projection PursuitThe Annals of Statistics, 1985
- Generalized Cross-Validation as a Method for Choosing a Good Ridge ParameterTechnometrics, 1979
- Control Methods Used in a Study of the VowelsThe Journal of the Acoustical Society of America, 1952