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Artigo de periodico
Boosting meta-learning with simulated data complexity measures (2020)
Garcia, Luís Paulo Faina ; Rivolli, Adriano ; Alcobaça, Edesio ; Lorena, Ana Carolina ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Intelligent Data Analysis. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS PARA PROCESSAMENTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARCIA, Luís Paulo Faina et al. Boosting meta-learning with simulated data complexity measures. Intelligent Data Analysis, v. 24, n. 5, p. 1011-1028, 2020Tradução . . Disponível em: https://doi.org/10.3233/IDA-194803. Acesso em: 13 nov. 2024.
APA
Garcia, L. P. F., Rivolli, A., Alcobaça, E., Lorena, A. C., & Carvalho, A. C. P. de L. F. de. (2020). Boosting meta-learning with simulated data complexity measures. Intelligent Data Analysis, 24( 5), 1011-1028. doi:10.3233/IDA-194803
NLM
Garcia LPF, Rivolli A, Alcobaça E, Lorena AC, Carvalho ACP de LF de. Boosting meta-learning with simulated data complexity measures [Internet]. Intelligent Data Analysis. 2020 ; 24( 5): 1011-1028.[citado 2024 nov. 13 ] Available from: https://doi.org/10.3233/IDA-194803
Vancouver
Garcia LPF, Rivolli A, Alcobaça E, Lorena AC, Carvalho ACP de LF de. Boosting meta-learning with simulated data complexity measures [Internet]. Intelligent Data Analysis. 2020 ; 24( 5): 1011-1028.[citado 2024 nov. 13 ] Available from: https://doi.org/10.3233/IDA-194803
Artigo de periodico
MFE: towards reproducible meta-feature extraction (2020)
Alcobaça, Edesio ; Siqueira, Felipe Alves; Rivolli, Adriano ; Garcia, Luís Paulo Faina ; Oliva, Jefferson Tales ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Journal of Machine Learning Research. Unidade: ICMC
Assunto: APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALCOBAÇA, Edesio et al. MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, v. 21, p. 1-5, 2020Tradução . . Disponível em: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf. Acesso em: 13 nov. 2024.
APA
Alcobaça, E., Siqueira, F. A., Rivolli, A., Garcia, L. P. F., Oliva, J. T., & Carvalho, A. C. P. de L. F. de. (2020). MFE: towards reproducible meta-feature extraction. Journal of Machine Learning Research, 21, 1-5. Recuperado de http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
NLM
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2024 nov. 13 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
Vancouver
Alcobaça E, Siqueira FA, Rivolli A, Garcia LPF, Oliva JT, Carvalho ACP de LF de. MFE: towards reproducible meta-feature extraction [Internet]. Journal of Machine Learning Research. 2020 ; 21 1-5.[citado 2024 nov. 13 ] Available from: http://www.jmlr.org/papers/volume21/19-348/19-348.pdf
Artigo de periodico
CRISPRcasIdentifier: machine learning for accurate identification and classification of CRISPR-Cas systems (2020)
Padilha, Victor Alexandre ; Alkhnbashi, Omer S ; Shah, Shiraz A ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Backofen, Rolf
Source: GigaScience. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, GENES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PADILHA, Victor Alexandre et al. CRISPRcasIdentifier: machine learning for accurate identification and classification of CRISPR-Cas systems. GigaScience, v. 9, n. 6, p. 1-12, 2020Tradução . . Disponível em: https://doi.org/10.1093/gigascience/giaa062. Acesso em: 13 nov. 2024.
APA
Padilha, V. A., Alkhnbashi, O. S., Shah, S. A., Carvalho, A. C. P. de L. F. de, & Backofen, R. (2020). CRISPRcasIdentifier: machine learning for accurate identification and classification of CRISPR-Cas systems. GigaScience, 9( 6), 1-12. doi:10.1093/gigascience/giaa062
NLM
Padilha VA, Alkhnbashi OS, Shah SA, Carvalho ACP de LF de, Backofen R. CRISPRcasIdentifier: machine learning for accurate identification and classification of CRISPR-Cas systems [Internet]. GigaScience. 2020 ; 9( 6): 1-12.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1093/gigascience/giaa062
Vancouver
Padilha VA, Alkhnbashi OS, Shah SA, Carvalho ACP de LF de, Backofen R. CRISPRcasIdentifier: machine learning for accurate identification and classification of CRISPR-Cas systems [Internet]. GigaScience. 2020 ; 9( 6): 1-12.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1093/gigascience/giaa062
Artigo de periodico
A two-stage regularization framework for heterogeneous event networks (2020)
Santos, Brucce Neves dos ; Rossi, Rafael Geraldeli ; Rezende, Solange Oliveira ; Marcacini, Ricardo Marcondes
Source: Pattern Recognition Letters. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, REDES SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Brucce Neves dos et al. A two-stage regularization framework for heterogeneous event networks. Pattern Recognition Letters, v. 138, p. 490-496, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.patrec.2020.08.019. Acesso em: 13 nov. 2024.
APA
Santos, B. N. dos, Rossi, R. G., Rezende, S. O., & Marcacini, R. M. (2020). A two-stage regularization framework for heterogeneous event networks. Pattern Recognition Letters, 138, 490-496. doi:10.1016/j.patrec.2020.08.019
NLM
Santos BN dos, Rossi RG, Rezende SO, Marcacini RM. A two-stage regularization framework for heterogeneous event networks [Internet]. Pattern Recognition Letters. 2020 ; 138 490-496.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1016/j.patrec.2020.08.019
Vancouver
Santos BN dos, Rossi RG, Rezende SO, Marcacini RM. A two-stage regularization framework for heterogeneous event networks [Internet]. Pattern Recognition Letters. 2020 ; 138 490-496.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1016/j.patrec.2020.08.019
Artigo de periodico
Reconstructing commuters network using machine learning and urban indicators (2019)
Spadon, Gabriel ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Rodrigues Junior, José Fernando ; Alves, Luiz Gustavo de Andrade
Source: Scientific Reports. Unidade: ICMC
Subjects: REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, MOBILIDADE URBANA, TRANSPORTE INTERURBANO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPADON, Gabriel et al. Reconstructing commuters network using machine learning and urban indicators. Scientific Reports, v. 9, p. 11801-1-11801-13, 2019Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-48295-x. Acesso em: 13 nov. 2024.
APA
Spadon, G., Carvalho, A. C. P. de L. F. de, Rodrigues Junior, J. F., & Alves, L. G. de A. (2019). Reconstructing commuters network using machine learning and urban indicators. Scientific Reports, 9, 11801-1-11801-13. doi:10.1038/s41598-019-48295-x
NLM
Spadon G, Carvalho ACP de LF de, Rodrigues Junior JF, Alves LG de A. Reconstructing commuters network using machine learning and urban indicators [Internet]. Scientific Reports. 2019 ; 9 11801-1-11801-13.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1038/s41598-019-48295-x
Vancouver
Spadon G, Carvalho ACP de LF de, Rodrigues Junior JF, Alves LG de A. Reconstructing commuters network using machine learning and urban indicators [Internet]. Scientific Reports. 2019 ; 9 11801-1-11801-13.[citado 2024 nov. 13 ] Available from: https://doi.org/10.1038/s41598-019-48295-x

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