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Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effect

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dc.contributor.authorSoares, Inês
dc.contributor.authorDias, Joana
dc.contributor.authorRocha, Humberto
dc.contributor.authorKhouri, Leila
dc.contributor.authorLopes, Maria do Carmo
dc.contributor.authorCosta Ferreira, Brigida
dc.date.accessioned2021-03-15T14:52:06Z
dc.date.available2021-03-15T14:52:06Z
dc.date.issued2016
dc.description.abstractSupervised learning algorithms have been widely used as predictors and applied in a myriad of studies. The accuracy of the classification algorithms is strongly dependent on the existence of large and balanced training sets. The existence of a reduced number of labeled data can deeply affect the use of supervised approaches. In these cases, semi-supervised learning algorithms can be a way to circumvent the problem.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSoares I., Dias J., Rocha H., Khouri L., do Carmo Lopes M., Ferreira B. (2016) Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effect. In: Kyriacou E., Christofides S., Pattichis C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_161pt_PT
dc.identifier.doi10.1007/978-3-319-32703-7_161pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/17493
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_161#citeaspt_PT
dc.subjectRadiotherapypt_PT
dc.subjectXerostomiapt_PT
dc.subjectSemi-supervised learningpt_PT
dc.subjectSmall databasespt_PT
dc.subjectUnbalanced datasetspt_PT
dc.titleSemi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effectpt_PT
dc.typebook part
dspace.entity.typePublication
person.familyNameCosta Ferreira
person.givenNameBrigida
person.identifier1167997
person.identifier.ciencia-idA61B-E07B-84B3
person.identifier.orcid0000-0001-7988-7545
person.identifier.scopus-author-id14050253300
rcaap.rightsclosedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublicationeac8b2c3-0ef3-48f5-a3c7-8ca796a098ae
relation.isAuthorOfPublication.latestForDiscoveryeac8b2c3-0ef3-48f5-a3c7-8ca796a098ae

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