Assuming we have -Input (instance) space X and output (label) space Y -Labeled data set L and unlabeled data set U (as mentioned before, no distinction is made between "unlabeled" and "test" data in the transductive learning setting) One could distinguish three types of learning paradigms: where f represents the induced model -Induction with unlabeled data The three learning paradigms clearly have different advantages and different application scenarios.
when it comes to exploiting unlabeled data, the tradeoff between the last two is not yet well understood.