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Título

A Lazy Learning Approach for Self-training

AutorArmengol, Eva
Palabras claveClassification
Machine learning
Lazy learning methods
Semi-supervised learning
Self-training
Fecha de publicación20-nov-2013
EditorSpringer
CitaciónLecture Notes in Computer Science, vol. 8234. 10th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2013, Barcelona, Spain, November 20-22, 2013. Proceedings, pp. 117-125
ResumenSelf-Training methods are a family of methods that use some supervised method to assign class labels to the unlabeled examples. The resulting model is useful to predict the classification of unseen new domain objects. Most common supervised methods used inside self-training are the inductive ones. In this paper we propose to use the lazy learning method LID to assign classes to the unlabeled examples. A lazy approach such as the one of LID allows to reason by similarity around the labeled examples. Thus, when an unlabeled example is classified as belonging to a class we are sure that it shares relevant features with some labeled examples. © 2013 Springer-Verlag.
URIhttp://hdl.handle.net/10261/132594
DOI10.1007/978-3-642-41550-0_11
Identificadoresdoi: 10.1007/978-3-642-41550-0_11
issn: 03029743
isbn: 978-364241549-4
Aparece en las colecciones: (IIIA) Comunicaciones congresos
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