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> L_{n} = \{x_{i}, z_{i}\}^{n}_{i=1} </math> | The learning set is denoted <math> L_{n} = \{x_{i}, z_{i}\}^{n}_{i=1} </math> | ||
| + | If <math> x_{i} </math> is labeled as <math> w_{i} </math>, then <math> z_{ik} = 1</math> | ||
| + | and <math> z_{il} = 0 </math> for <math> l \not= k </math>. | ||
Revision as of 20:07, 8 October 2010
Minimum entropy regularization can be applied to any model of posterior distribution. The learning set is denoted If is labeled as , then and for .
