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| <math> | | <math> |
− | C(\mathbf{\theta}, \lambda; L_{n}) = -\frac{1}{n} \sum^{n}_{i=1} \sum^{K}_{k=1} P(Y=w_{k}|x_{i}, z_{i})\text{log}P(Y=w_{k}|x_{i},z_{i}) | + | C(\theta, \lambda; L_{n}) = L(\theta; \mathcal{L}_{n}) - \lambda H(Y|X,Z; \mathcal{L}_{n}) |
| </math> | | </math> |
Revision as of 20:24, 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