RBFM(1)

NAME
rbfm - Radial Basis Function Mapper

SYNOPSIS
A program for training and testing a radial basis function mapper using using Gaussian hidden nodes initialized using the clustering program kmeans. A weighted sum of Gaussian hidden node outputs is calculated for each mapping output. The weights are trained in order to minimize the error in matching the outputs in the training set.

rbfm [ -minvar x ] [ -max_ratio x ] [ -hspread x ]
[ -exhspread x ] [ -bias ] [ -fclparam file ]
[ -mapper_options ]

EXAMPLE
kmeansm -train -create -fparam kmeans.param \
-pathdata $LNKHOME/data/map \
-fdescribe gauss_map.train.defaults \
-finput gauss_map.train -npatterns 338

rbfm -bias -fclparam kmeans.param -train -create \
-finput gauss_map.train \
-fdescribe gauss_map.train.defaults -npatterns 338

DESCRIPTION
This rbf mapping program works in the same way as the rbf program for classification data. The only significant differences are that this program reads mapping data and that the basis function outputs are not normalized to sum to one.

OPTIONS
The algorithm specific options are those available for the program rbf except:

    -no_norm_outputs
        The outputs are never normalized to sum to one.

-mapper_options
    rbfm accepts the general mapper arguments described on the mapper(1) manual page. Certain arguments are not used by the program, and so are not available.

    Unavailable arguments from general list:

    -ninputs, -features, -normalize, -fnorm
        The settings for these flags are read from the cluster file used to initialize the centers.

REFERENCE
Kenney Ng, A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers, Bachelor's and Master's Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, May 1990. Pages 16-23.

SEE ALSO
mapper(1) rbf(1) kmeansm(1) rbfm_plot_map(1)

HISTORY
First written 5 November 1992 by Linda Kukolich

BUGS
Send bug reports to Linda Kukolich (kukolich@sst.ll.mit.edu)