Stochastic discrimination is a general methodology
for constructing classifiers appropriate for
pattern recognition. It is based on combining
arbitrary numbers of very weak components, which
are usually generated by some pseudorandom
process, and it has the property that the very
complex and accurate classifiers produced in this
way retain the ability, characteristic of their
weak component pieces, to generalize to new data
as complexity increases. These utilities provide
an implementation of this algorithm.
