Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
The Impact of Reliability Evaluation on a Semi-supervised Learning Approach
verfasst von : Pasquale Foggia, Gennaro Percannella, Carlo Sansone, Mario Vento
Erschienen in: Image Analysis and Processing – ICIAP 2009
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In
self-training
methods, unlabeled samples are first assigned a provisional label by the classifier, and then used to extend the training set of the classifier itself. For this latter step it is important to choose only the samples whose classification is likely to be correct, according to a suitably defined reliability measure.
In this paper we want to study to what extent the choice of a particular technique for evaluating the classification reliability can affect the learning performance. To this aim, we have compared five different reliability evaluators on four publicly available datasets, analyzing and discussing the obtained results.