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Título

Semi-Supervised Learning with Partial Domain Models

AutorArmengol, Eva
Palabras clavePartial domain models
Lazy learning
Machine learning
Semi-supervised learning
Self-training
Fecha de publicación2013
EditorIOS Press
CitaciónFrontiers in Artificial Intelligence and Applications 256. Artificial Intelligence Research and Development, Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence, Vic, Catalonia, Spain, October 23-25, 2013, pp. 151-154.
ResumenSelf-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.
URIhttp://hdl.handle.net/10261/134116
DOI10.3233/978-1-61499-320-9-151
Identificadoresdoi: 10.3233/978-1-61499-320-9-151
isbn: 978-1-61499-319-3
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