  Semi-supervised learning deals with the problem of how, if possible, to take
advantage of a huge amount of not classified data, to perform classification,
in situations when, typically, the labelled data are few. Even though this is
not always possible (it depends on how useful is to know the distribution of
the unlabelled data in the inference of the labels), several algorithm have
been proposed recently. A new algorithm is proposed, that under almost
neccesary conditions, attains asymptotically the performance of the best
theoretical rule, when the size of unlabeled data tends to infinity. The set of
necessary assumptions, although reasonables, show that semi-parametric
classification only works for very well conditioned problems.
