HISTOGRAM_LNK(1)

NAME
histogram_lnk - Histogram Classifier

SYNOPSIS
Program for training and testing of a Histogram classifier which models the density function of each class using histograms.

histogram_lnk [ -grand_bins n ] [ -bin_type n ]
[ -nbins n ] [ -nBinsPerClass n[,n]... ]
[ -min_var x ] [ -range_factor x ]
[ -user_min x ] [ -user_max x ] [ -use_gauss ]
[ -classifier_options ]

EXAMPLE
histogram_lnk -grand_bins 0 -nbins 4 -train -create \
-pathdata $LNKHOME/data/class -finput vowel.train \
-fdescribe vowel.defaults -npatterns 338 \
-fparam histogram.param

histogram_lnk -pathdata $LNKHOME/data/class -finput vowel.eval \
-fdescribe vowel.defaults -npatterns 166 \
-fparam histogram.param

DESCRIPTION
Estimates the likelihood of each class by creating a set of histograms for each input feature. Each input feature dimension is divided into a number of bins. The likelihood assigned to each bin is proportional to the number of training patterns that fall in that bin divided by the bin width. In testing, these likelihoods are multiplied across input features to give an overall likelihood for each class. An optional per class diagonal Gaussian classifier can be used to determine the class of all patterns that fall outside the histogram bins.

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

-grand_bins n

    0   Bin each class separately and determine bin ends for each input feature independently.

    1   Generate one set of bins for all classes, determining histogram ends for each input feature based on the range of the training patterns.

    2   Generate one set of equally spaced bins for all classes, setting the histogram ends for all input features based on the user_min and user_max parameters.

-bin_type n
    This flag determines the positions of the bins ends within histograms. The flag has effect when grand_bins is set to 0 or 1. When grand_bins is set to 2, only evenly spaced bins are used.

    0 - evenly spaced bins
        When setting the bin ends for bins in a histogram, space the bin ends evenly making all the bins an equal width.

    1 - variable spaced bins
        Position the bin ends to cause an equal number of patterns to fall in each bin.

-nbins n
    Split each histogram into n bins. This will generate a total of noutputs*nbins histogram bins when the grand_bins flag is set to 0. When the grand_bins flag is set to 1 or 2, this will generate a total of nbins.

-nBinsPerClass n[,n]...
    When grand_bins is set to 0, split the histograms for each class into the given number of bins.

-min_var x
    x is the minimum variance permitted for the Gaussian classifier which finds the class of patterns which fall outside the histograms.

-range_factor x
    Bins normally extend to cover all training patterns. This range can be expanded using the range factor. When the -grand_bins flag is set to 0 or 1, range from the first to last bin end is multiplied by this factor. When equal width bins are used (-bin_type 0) this scales all the histogram bins by the range factor. When bins with equal numbers of patterns are used, this expands the range of the first and last bins of each histogram but leaves the others alone.

-user_min x
    When the -grand_bins flag is set to 2, x is the starting value for the histograms for all input features.

-user_max x
    When the -grand_bins flag is set to 2, x is the final value for the histograms for all input features.

-use_gauss
    When testing a histogram classifier, use a diagonal covariance Gaussian classifier with one covariance matrix per class to determine the class of patterns which fall outside the histograms. If this flag is not set, those patterns which fall outside are labeled as unknown.

-classifier_options
    histogram_lnk accepts the general classifier arguments described on the classifier(1) manual page.

    Notes about argments from general list:

    -random
        This flag has an effect only when adjust_train_priors is set.

REFERENCE
Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973. Pages 24-31.

SEE ALSO
classifier(1), histogram2c(1), histogram_feat_sel(1), histogram_plot_bound(1)

HISTORY
First written 18 April 1995 by Linda Kukolich

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