IRBFM(1)

NAME
irbfm - Incremental Radial Basis Function Mapper

SYNOPSIS
A program for training and testing a radial basis function mapper using Gaussian hidden nodes initialized using the clustering program kmeansm. A weighted sum of Gaussian hidden node outputs is calculated for each mapping output. The connection weights and basis function means and variances are trained using gradient descent.

irbfm [ -fclparam file ] [ -epochs n ] [ -minvar x ]
[ -max_magnitude x ] [ -hspread x ] [ -exhspread x ]
[ -bias ] [ -grand ] [ weight_eta x ] [ mean_eta x ]
[ var_eta x ] [ -mapper_options ]

EXAMPLE
kmeansm -pathdata $LNKHOME/data/map -fparam kmeans.param \
-fdescribe gauss_map.train.defaults \
-finput gauss_map.train -npatterns 200 -nraw 1 -noutputs 1

irbfm -fclparam kmeans.param -epochs 10 -train \
-create -pathdata $LNKHOME/data/map \
-finput gauss_map.train \
-fdescribe gauss_map.train.defaults -npatterns 200 \
-nraw 1 -noutputs 1 -fparam irbfm.param

irbfm -fparam irbfm.param -pathdata $LNKHOME/data/map \
-finput gauss_map.test -fdescribe gauss_map.test.defaults \
-npatterns 100 -nraw 1 -noutputs 1

DESCRIPTION
This incremental radial basis function mapping program works in the same way as the irbf 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. Also, only the squared error cost function is available.

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

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

    -cost_func, -cost_param
        Only squared error is available.

-mapper_options
    irbfm 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 and Richard P. Lippman, A Comparative Study of the Practival Characteristics of Neural Network and Conventional Pattern Classifiers, MIT Lincoln Laboratory, Technical Report 894, 1991.

SEE ALSO
mapper(1), irbf(1), irbfm_plot_map(1), kmeansm(1)

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