Entropy Minimization for Semi-supervised Learning

Minimum entropy regularization can be applied to any model of posterior distribution. The learning set is denoted $L_{n}=\{x_{i},z_{i}\}_{i=1}^{n}$, where $z_{i}\in \{0,1\}^{K}$: If $x_{i}$ is labeled as $w_{i}$, then $z_{ik}=1$ and $z_{il}=0$ for $l\not =k$; if $x_{i}$ is unlabeled, then $z_{il}=1$ for $l=1\dots K$.