CKNN_LNK(1)

NAME
cknn_lnk - Condensed Nearest Neighbor Classifier

SYNOPSIS
Program for training and testing a K Nearest Neighbor classifier which uses a reduced set of stored exemplar training patterns.

cknn_lnk [ -k n ] [ -epochs n ] [ -classifier_options ]

EXAMPLE
cknn_lnk -epochs 3 -k 1 -train -create \
-pathdata $LNKHOME/data/class -finput vowel.train \
-fdescribe vowel.defaults -npatterns 338 -fparam cknn.param

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

DESCRIPTION
In training, stores patterns from the training set for use in K Nearest Neighbor classification. A pattern is stored during training if it would be classified incorrectly by those patterns which have already been stored.

In testing, find the K stored patterns nearest the test pattern. The class selected for the test pattern is the one with the most exemplars among the K nearest neighbors. Ties between classes are broken randomly.

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

-k n
    n is the value for k, the number of nearest neighbors to use in training. The k used in testing is the one set in training for that model. If k is greater than 1, it is possible that some training patterns will be stored more than once.

-epochs n
    n is the number of times to run through all of the data in the input file during training. Any pattern that is incorrectly classified during an epoch is stored. This flag has no effect during testing.

-classifier_options
    cknn_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:

    -adjust_test_priors -test_uniform_priors -test_priors_list
        Because cknn_lnk does nearest neighbor classification, there are no outputs to scale for adjusting the priors during testing.

    -log_outputs
        cknn_lnk does not currently store outputs in the error file.

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 49-52.

SEE ALSO
classifier(1), cknn2c(1), cknn_feat_sel(1), cknn_plot_bound(1)

HISTORY
First written 26 May 1992 by Linda Kukolich

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