  We explore a novel approach to semi-supervised learning. This approach is
contrary to the common approach in that the unlabeled examples serve to
"muffle," rather than enhance, the guidance provided by the labeled examples.
We provide several variants of the basic algorithm and show experimentally that
they can achieve significantly higher AUC than boosted trees, random forests
and logistic regression when unlabeled examples are available.
