Weakly supervised learning through rank-based contextual measures

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPresotto, João Gabriel Camacho-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorde Sá, Nikolas Gomes-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Autor(es): dc.creatorPapa, João Paulo-
Data de aceite: dc.date.accessioned2025-08-21T17:15:58Z-
Data de disponibilização: dc.date.available2025-08-21T17:15:58Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICPR48806.2021.9412596-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233278-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233278-
Descrição: dc.descriptionMachine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive growth, becoming ubiquitous in diverse domains. Due to the huge increase in multimedia data collections and the lack of labeled data in several scenarios, creating methods capable of exploiting the unlabeled data and operating under weakly supervision is imperative. In this work, we propose a rank-based model to exploit contextual information encoded in the unlabeled data in order to perform weakly supervised classification. We employ different rank-based correlation measures for identifying strong similarities relationships and expanding the labeled set in an unsupervised way. Subsequently, the extended labeled set is used by a classifier to achieve better accuracy results. The proposed weakly supervised approach was evaluated on multimedia classification tasks, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 4 public image datasets and different features. Very positive gains were achieved in comparison with various semi-supervised and supervised classifiers taken as baselines when considering the same amount of labeled data.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Statistics Applied Math. and Computing UNESP São Paulo State University, SP-
Descrição: dc.descriptionSchool of Sciences UNESP São Paulo State University, SP-
Descrição: dc.descriptionDepartment of Statistics Applied Math. and Computing UNESP São Paulo State University, SP-
Descrição: dc.descriptionSchool of Sciences UNESP São Paulo State University, SP-
Descrição: dc.descriptionFAPESP: 2019/04754-6-
Formato: dc.format5752-5759-
Idioma: dc.languageen-
Relação: dc.relationProceedings - International Conference on Pattern Recognition-
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Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectRank correlation measure-
Palavras-chave: dc.subjectWeak supervision-
Título: dc.titleWeakly supervised learning through rank-based contextual measures-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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