IRBF_LNK(1)

NAME
irbf_lnk - Incremental Radial Basis Function Classifier

SYNOPSIS
A program for training and testing a radial basis function classifier using Gaussian hidden nodes with means and variances initialized using a clustering program (usually kmeans or em_clus). Calculates a weighted sum of Gaussian outputs for each class. The weights, means, and variances are trained using gradient descent. The squared error or top two difference cost function can be selected.

irbf_lnk [ -fclparam file ] [ -epochs n ] [ -hspread x ]
[ -exhspread x ] [ -max_magnitude x ]
[ -minvar x ] [ -bias ] [ -grand ] [ -weight_eta x ]
[ -mean_eta x ] [ -var_eta x ] [ -cost_func n ]
[ -cost_param x ] [ -no_norm_outputs ]
[ -classifier_options ]

EXAMPLE
kmeans -train -create -ncenters 16 -fparam kmeans.param \
-pathdata $LNKHOME/data/class -fdescribe vowel.defaults \
-finput vowel.train -npatterns 338

irbf_lnk -fclparam kmeans.param -train -create -epochs 10 \
-pathdata $LNKHOME/data/class -finput vowel.train \
-fdescribe vowel.defaults -npatterns 338 \
-fparam irbf.param -mean_eta .001 -var_eta .01 \
-weight_eta .1

irbf_lnk -fparam irbf.param -pathdata $LNKHOME/data/class \
-finput vowel.eval -fdescribe vowel.defaults \
-npatterns 166 -exhspread 3

DESCRIPTION
In testing, the incremental radial basis function classifier behaves exactly as the single pass rbf classifier. For each pattern, the output of each basis function is found. A weighted sum of these outputs is then found for each class. Hidden node outputs are normalized to sum to one, if this was done during training. The class with the highest result is selected.

Because the weights, means, and variances are all trained, the incremental rbf requires multiple passes through the data during training. Three step sizes are used in the training, one each for the weights, means, and variances. The cost function being minimized during training can be selected by the user. The cost function used by the single pass rbf is squared error.

OPTIONS
-- Prints a list of all flags and arguments.

-fclparam filename
    The file in pathexp with the initial centers.

-epochs n
    n is the number passes through all of the data to be done during training.

-hspread x
    The variances of the clusters are multiplied by x during training and testing.

-exhspread x
    The variances of the clusters are multiplied by x during testing.

-max_magnitude x
    x is the maximum absolute random value of the output weights set when first initializing an irbf classifier.

-minvar x
    x is the minimum value of the diagonal variances in hidden nodes.

-bias
    Add a bias hidden node with a constant output.

-grand
    Have all clusters share a set of grand variances, one variance per input feature. The default is to train separate variances for all of the clusters.

-weight_eta x
    x is the step size used when training the weights.

-mean_eta x
    x is the step size used when training the means of the clusters.

-var_eta x
    x is the step size used when training the diagonal covariance matrices of the clusters.

-cost_func n
    n determines the output cost function.

    0 Squared Error

    4 Top Two Difference

-cost_param x
    x is a parameter required by the selected cost function. For top two difference the cost parameter is the steepness of the sigmoid applied to the difference between the top two outputs. The steepness ranges from 0.0 to 1.0.

-no_norm_outputs
    Do not normalize the hidden node outputs to sum to one.

-classifier_options
    irbf_lnk accepts the general classifier arguments described on the classifier(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.

    -log_outputs

REFERENCE
Kenney Ng and Richard P. Lippmann, A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers, MIT Lincoln Laboratory, Technical Report 894, 1991.

SEE ALSO
classifier(1), irbf2c(1), irbf_feat_sel(1), irbf_plot_bound(1), plot_irbf(1), rbf_lnk(1), kmeans_lnk(1), em_clus_lnk(1), lead_clus_lnk(1), ran_clus_lnk(1)

HISTORY
First written 2 July 1993 by Linda Kukolich
Modified 22 June 1994, added more flags to example program, more programs to SEE ALSO

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