Combining clustering and active learning for the detection and learning of new image classes (2019)
- Authors:
- USP affiliated authors: PONTI, MOACIR ANTONELLI - ICMC ; HRUSCHKA, EDUARDO RAUL - EP
- Unidades: ICMC; EP
- DOI: 10.1016/j.neucom.2019.04.070
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE IMAGEM
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Neurocomputing
- ISSN: 0925-2312
- Volume/Número/Paginação/Ano: v. 358, p. 150-165, Sep. 2019
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
COLETTA, Luiz Fernando Sommaggio et al. Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing, v. 358, p. Se 2019, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2019.04.070. Acesso em: 02 maio 2024. -
APA
Coletta, L. F. S., Ponti, M. A., Hruschka, E. R., Acharya, A., & Ghosh, J. (2019). Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing, 358, Se 2019. doi:10.1016/j.neucom.2019.04.070 -
NLM
Coletta LFS, Ponti MA, Hruschka ER, Acharya A, Ghosh J. Combining clustering and active learning for the detection and learning of new image classes [Internet]. Neurocomputing. 2019 ; 358 Se 2019.[citado 2024 maio 02 ] Available from: https://doi.org/10.1016/j.neucom.2019.04.070 -
Vancouver
Coletta LFS, Ponti MA, Hruschka ER, Acharya A, Ghosh J. Combining clustering and active learning for the detection and learning of new image classes [Internet]. Neurocomputing. 2019 ; 358 Se 2019.[citado 2024 maio 02 ] Available from: https://doi.org/10.1016/j.neucom.2019.04.070 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
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Informações sobre o DOI: 10.1016/j.neucom.2019.04.070 (Fonte: oaDOI API)
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