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Artigo de periodico
A network-based positive and unlabeled learning approach for fake news detection (2022)
Souza, Mariana Caravanti de ; Nogueira, Bruno Magalhães ; Rossi, Rafael Geraldeli ; Marcacini, Ricardo Marcondes ; Santos, Brucce Neves dos ; Rezende, Solange Oliveira
Source: Machine Learning. Unidade: ICMC
Subjects: FAKE NEWS, PROCESSAMENTO DE TEXTO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Mariana Caravanti de et al. A network-based positive and unlabeled learning approach for fake news detection. Machine Learning, v. 111, n. 10, p. 3549-3592, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10994-021-06111-6. Acesso em: 16 maio 2024.
APA
Souza, M. C. de, Nogueira, B. M., Rossi, R. G., Marcacini, R. M., Santos, B. N. dos, & Rezende, S. O. (2022). A network-based positive and unlabeled learning approach for fake news detection. Machine Learning, 111( 10), 3549-3592. doi:10.1007/s10994-021-06111-6
NLM
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Santos BN dos, Rezende SO. A network-based positive and unlabeled learning approach for fake news detection [Internet]. Machine Learning. 2022 ; 111( 10): 3549-3592.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-021-06111-6
Vancouver
Souza MC de, Nogueira BM, Rossi RG, Marcacini RM, Santos BN dos, Rezende SO. A network-based positive and unlabeled learning approach for fake news detection [Internet]. Machine Learning. 2022 ; 111( 10): 3549-3592.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-021-06111-6
Artigo de periodico
Clustered and deep echo state networks for signal noise reduction (2022)
Oliveira Junior, Laercio de; Stelzer, Florian; Liang, Zhao
Source: Machine Learning. Unidade: FFCLRP
Subjects: REDES COMPLEXAS, REDES NEURAIS, SISTEMAS DINÂMICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA JUNIOR, Laercio de e STELZER, Florian e LIANG, Zhao. Clustered and deep echo state networks for signal noise reduction. Machine Learning, v. 111, n. 8, p. 2885-2904, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10994-022-06135-6. Acesso em: 16 maio 2024.
APA
Oliveira Junior, L. de, Stelzer, F., & Liang, Z. (2022). Clustered and deep echo state networks for signal noise reduction. Machine Learning, 111( 8), 2885-2904. doi:10.1007/s10994-022-06135-6
NLM
Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
Vancouver
Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
Artigo de periodico
Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders (2021)
Converse, Geoffrey ; Cúri, Mariana ; Oliveira, Suely; Templin, Jonathan
Source: Machine Learning. Unidade: ICMC
Subjects: MÉTODO DE MONTE CARLO, REDES NEURAIS, TEORIA DE RESPOSTA AO ITEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CONVERSE, Geoffrey et al. Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders. Machine Learning, v. 110, p. 1463-1480, 2021Tradução . . Disponível em: https://doi.org/10.1007/s10994-021-06005-7. Acesso em: 16 maio 2024.
APA
Converse, G., Cúri, M., Oliveira, S., & Templin, J. (2021). Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders. Machine Learning, 110, 1463-1480. doi:10.1007/s10994-021-06005-7
NLM
Converse G, Cúri M, Oliveira S, Templin J. Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders [Internet]. Machine Learning. 2021 ; 110 1463-1480.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-021-06005-7
Vancouver
Converse G, Cúri M, Oliveira S, Templin J. Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders [Internet]. Machine Learning. 2021 ; 110 1463-1480.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-021-06005-7
Artigo de periodico
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning (2020)
Rivolli, Adriano ; Read, Jesse; Soares, Carlos; Pfahringer, Bernhard ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Machine Learning. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RIVOLLI, Adriano et al. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning, v. 109, n. 8, p. 1509-1563, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10994-020-05879-3. Acesso em: 16 maio 2024.
APA
Rivolli, A., Read, J., Soares, C., Pfahringer, B., & Carvalho, A. C. P. de L. F. de. (2020). An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Machine Learning, 109( 8), 1509-1563. doi:10.1007/s10994-020-05879-3
NLM
Rivolli A, Read J, Soares C, Pfahringer B, Carvalho ACP de LF de. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning [Internet]. Machine Learning. 2020 ; 109( 8): 1509-1563.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-020-05879-3
Vancouver
Rivolli A, Read J, Soares C, Pfahringer B, Carvalho ACP de LF de. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning [Internet]. Machine Learning. 2020 ; 109( 8): 1509-1563.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-020-05879-3
Artigo de periodico
An investigation into feature construction to assist word sense disambiquation (2009)
Specia, Lucia; Srinivasan, Ashwin; Joshi, Sachindra; Ramakrishnan, Ganesh; Nunes, Maria das Graças Volpe
Source: Machine Learning. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPECIA, Lucia et al. An investigation into feature construction to assist word sense disambiquation. Machine Learning, v. 76, n. 1, p. 109-136, 2009Tradução . . Disponível em: https://doi.org/10.1007/s10994-009-5114-x. Acesso em: 16 maio 2024.
APA
Specia, L., Srinivasan, A., Joshi, S., Ramakrishnan, G., & Nunes, M. das G. V. (2009). An investigation into feature construction to assist word sense disambiquation. Machine Learning, 76( 1), 109-136. doi:10.1007/s10994-009-5114-x
NLM
Specia L, Srinivasan A, Joshi S, Ramakrishnan G, Nunes M das GV. An investigation into feature construction to assist word sense disambiquation [Internet]. Machine Learning. 2009 ; 76( 1): 109-136.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-009-5114-x
Vancouver
Specia L, Srinivasan A, Joshi S, Ramakrishnan G, Nunes M das GV. An investigation into feature construction to assist word sense disambiquation [Internet]. Machine Learning. 2009 ; 76( 1): 109-136.[citado 2024 maio 16 ] Available from: https://doi.org/10.1007/s10994-009-5114-x

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