Difference between revisions of "Unlabeled examples"

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An unlabeled example is one that has not been identified or categorized prior to presentation to the learner. The reason for using unlabeled examples is that they are often much less expensive to create or find since they can be geenrated automatically.
 
An unlabeled example is one that has not been identified or categorized prior to presentation to the learner. The reason for using unlabeled examples is that they are often much less expensive to create or find since they can be geenrated automatically.
  
Unlabeled examples are interesting for learning because they force the learner to create a label and therefore may result in enhanced learnign compared to labeled examples. This has also been describedas the testing effect, which is the beenfit to testing (labeling) as opposed to passive study.
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Unlabeled examples are interesting for learning because they force the learner to create a label and therefore may result in enhanced learning compared to labeled examples. This has also been describedas the testing effect, which is the beenfit to testing (labeling) as opposed to passive study.
  
 
A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.  
 
A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.  

Revision as of 13:25, 27 November 2006

An unlabeled example is one that has not been identified or categorized prior to presentation to the learner. The reason for using unlabeled examples is that they are often much less expensive to create or find since they can be geenrated automatically.

Unlabeled examples are interesting for learning because they force the learner to create a label and therefore may result in enhanced learning compared to labeled examples. This has also been describedas the testing effect, which is the beenfit to testing (labeling) as opposed to passive study.

A.Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.