Experiments with Some Algorithms that Find
Central Solutions for Pattern Classification

In two-class pattern recognition, it is a standard
technique to have an algorithm finding hyperplanes
which separates the two classes in a linearly separable training
set.  The traditional methods find a hyperplane which separates all
points in the other, but such a hyperplane is not necessarily centered
in the empty space between the two classes.  Since a central
hyperplane does not favor one class or the other, it should have
a lower error rate in classifying new points and is therefore better
than a noncentral hyperplane.  Six algorithms for finding central
hyperplanes are tested on three data sets.  Although frequently
used practice, the modified relaxation algorithm is very poor. 
Three algorithms which are defined in the paper are found to be
quite good.

CACM March, 1979

Slagle, J.

Pattern recognition, pattern classification, linear discriminants, central
hyperplanes, centering, centrality criteria, dead zone, hyperplane,
linearly separable, relaxation algorithm, accelerated relaxation

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CA790303 DH April 12, 1979  3:20 PM

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