You train your classifier on L
. You can firstly perform cross-validation to fit some method parameters P
. With parameters P
you construct model M
, from labeled data L
. You then use the model M
to label unlabeled data U
. You join the examples from U
(with heighest confidence in assigned class) with L
. You then repeat the procedure until all the examples are classiied.
-edit-
I think the most appropriate approach is the third one. But I may not understand it right, so here goes.
You split L
to L_train
and L_test
. You train your classifier using L_train
and you also use this classifier to classify U
(as per methodology I described above). From union of labeled U
and L_train
you construct a new classifier, and with it you classify L_test
. The differences in these classification can be used for evaluation measures (classification accuracy, ...).