Difference between revisions of "Entropy Minimization for Semi-supervised Learning"
From Cohen Courses
Jump to navigationJump to searchPastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 1: | Line 1: | ||
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>, |
| + | 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> | ||
| − | and <math> z_{il} = 0 </math> for <math> l \not= k </math>. | + | and <math> z_{il} = 0 </math> for <math> l \not= k </math>; if <math> x_{i} </math> is unlabeled, |
| + | then <math> z_{il} = 1 </math> for <math> l = 1 \dots K </math>. | ||
Revision as of 20:11, 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 .
