Difference between revisions of "Entropy Minimization for Semi-supervised Learning"
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Minimum entropy regularization can be applied to any model of posterior distribution. | Minimum entropy regularization can be applied to any model of posterior distribution. | ||
− | The learning set is denoted <math> | + | The learning set is denoted <math> \mathcal{L}_{n} = \{X^{(i)}, Z^{(i)}\}^{n}_{i=1} </math>, |
where <math> z_{i} \in \{0,1\}^K </math>: | where <math> z_{i} \in \{0,1\}^K </math>: | ||
If <math> x_{i} </math> is labeled as <math> w_{i} </math>, then <math> z_{ik} = 1</math> | If <math> x_{i} </math> is labeled as <math> w_{i} </math>, then <math> z_{ik} = 1</math> |
Revision as of 20:38, 8 October 2010
Minimum entropy regularization can be applied to any model of posterior distribution.
The learning set is denoted , where : If is labeled as , then and for ; if is unlabeled, then for .
The conditional entropy of class labels conditioned on the observed variables:
The posterior distribution is defined as