  There has been increasing attention to semi-supervised learning (SSL)
approaches in machine learning to forming a classifier in situations where the
training data for a classifier consists of a limited number of classified
observations but a much larger number of unclassified observations. This is
because the procurement of classified data can be quite costly due to high
acquisition costs and subsequent financial, time, and ethical issues that can
arise in attempts to provide the true class labels for the unclassified data
that have been acquired. We provide here a review of statistical SSL approaches
to this problem, focussing on the recent result that a classifier formed from a
partially classified sample can actually have smaller expected error rate than
that if the sample were completely classified.
