Introduction to radial basis function networks

This document is an introduction to radial basis function RBF networks a type of arti cial neural network for application to problems of supervised learning e g regression classi cation and time series prediction It is now only available in PostScript an older and now unsupported hyper text ver sion may be available for a while longer The document was rst published in along with a package of Matlab functions implementing the methods described In a new document Recent Advances in Radial Basis Function Networks became available with a second and improved version of the Matlab package mjo anc ed ac uk www anc ed ac uk mjo papers intro ps www anc ed ac uk mjo intro intro html www anc ed ac uk mjo software rbf zip www anc ed ac uk mjo papers recad ps www anc ed ac uk mjo software rbf zip

[1]  C. L. Mallows Some comments on C_p , 1973 .

[2]  David M. Allen,et al.  The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction , 1974 .

[3]  R. R. Hocking The analysis and selection of variables in linear regression , 1976 .

[4]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[5]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[6]  Ronald D. Snee [Developments in Linear Regression Methodology: 1959-1982]: Discussion , 1983 .

[7]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[8]  Editors , 1986, Brain Research Bulletin.

[9]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[10]  John C. Platt A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[11]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[12]  Chong Gu,et al.  Minimizing GCV/GML Scores with Multiple Smoothing Parameters via the Newton Method , 1991, SIAM J. Sci. Comput..

[13]  John E. Moody,et al.  The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems , 1991, NIPS.

[14]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[15]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[16]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[17]  Warren S. Sarle,et al.  Neural Networks and Statistical Models , 1994 .

[18]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[19]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[20]  Ashok N. Srivastava,et al.  Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..

[21]  Mark J. L. Orr Local Smoothing of Radial Basis Function Networks , 1995 .

[22]  CentresMark,et al.  Regularisation in the Selection of Radial Basis Function , 1995 .

[23]  J. Friedman,et al.  Predicting Multivariate Responses in Multiple Linear Regression , 1997 .