Graph construction based on labeled instances for semi-supervised learning (2014)
- Authors:
- Autor USP: LOPES, ALNEU DE ANDRADE - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICPR.2014.428
- Subjects: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; REDES NEURAIS
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Publisher place: Los Alamitos
- Date published: 2014
- Source:
- Título do periódico: Proceedings
- ISSN: 1051-4651
- Conference titles: International Conference on Pattern Recognition - ICPR
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
BERTON, Lilian e LOPES, Alneu de Andrade. Graph construction based on labeled instances for semi-supervised learning. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/ICPR.2014.428. Acesso em: 21 maio 2024. -
APA
Berton, L., & Lopes, A. de A. (2014). Graph construction based on labeled instances for semi-supervised learning. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/ICPR.2014.428 -
NLM
Berton L, Lopes A de A. Graph construction based on labeled instances for semi-supervised learning [Internet]. Proceedings. 2014 ;[citado 2024 maio 21 ] Available from: https://doi.org/10.1109/ICPR.2014.428 -
Vancouver
Berton L, Lopes A de A. Graph construction based on labeled instances for semi-supervised learning [Internet]. Proceedings. 2014 ;[citado 2024 maio 21 ] Available from: https://doi.org/10.1109/ICPR.2014.428 - Efficient identification of duplicate bibliographical references
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- A graph-based bagging
- An open source tool for crowd-sourcing the manual annotation of texts
- Link prediction in graph construction for supervised and semi-supervised learning
- Exploring data classification with k-associated network
- Bipartite graph for topic extraction
- Structural link prediction using community information on Twitter
- Graph-based cross-validated committees ensembles
- Data visualization and relevance feedback applied to information retrieval
Informações sobre o DOI: 10.1109/ICPR.2014.428 (Fonte: oaDOI API)
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