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Self-Training methods are a family of methods that uses some supervised method (commonly an inductive learning method) to assign class labels to unlabeled examples. The resulting inductive model is useful to predict the classification of unseen new domain objects. In this paper we propose to use a lazy learning method called LID, capable of producing descriptions similar to the ones from inductive learning methods. In the experiments we prove that this partial domain is very useful to predict the classification of unseen objects.
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