  We consider the task of learning a classifier from the feature space
$\mathcal{X}$ to the set of classes $\mathcal{Y} = \{0, 1\}$, when the features
can be partitioned into class-conditionally independent feature sets
$\mathcal{X}_1$ and $\mathcal{X}_2$. We show the surprising fact that the
class-conditional independence can be used to represent the original learning
task in terms of 1) learning a classifier from $\mathcal{X}_2$ to
$\mathcal{X}_1$ and 2) learning the class-conditional distribution of the
feature set $\mathcal{X}_1$. This fact can be exploited for semi-supervised
learning because the former task can be accomplished purely from unlabeled
samples. We present experimental evaluation of the idea in two real world
applications.
