Learning from partially labeled data

The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training points (x alone). For the labeled points, supervised learning techniques apply, but they cannot take advantage of the unlabeled points. On the other hand, unsupervised techniques can model the unlabeled data distribution, but do not exploit the labels. Thus, this task falls between traditional supervised and unsupervised learning.