Difference between revisions of "Entropy Minimization for Semi-supervised Learning"
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\begin{alignat}{2} | \begin{alignat}{2} | ||
C(\theta, \lambda; L_{n}) & = L(\theta; \mathcal{L}_{n}) - \lambda H(Y|X,Z; \mathcal{L}_{n}) \\ | C(\theta, \lambda; L_{n}) & = L(\theta; \mathcal{L}_{n}) - \lambda H(Y|X,Z; \mathcal{L}_{n}) \\ | ||
− | & = \sum^{n}_{i=1} \text{log}(\sum^{K}_{k=1} | + | & = \sum^{n}_{i=1} \text{log}(\sum^{K}_{k=1} Z_{ik}P(Y^{i}=w_{k}|X^{i})) + \lambda \sum^{n}_{i=1} \sum_{k=1}^{K} P(Y^{i}=w_{k}|X^{i}, Z^{i}) \text{log} P(Y^{i}=w_{k}|X^{i}, Z^{i}) |
\end{alignat} | \end{alignat} | ||
</math> | </math> |
Revision as of 20:35, 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