Filtros : "MONARD, MARIA CAROLINA" "Indexado no Current Contents" Removido: "Conference on Women Work and Computarization" Limpar

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  • Source: Information Sciences. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, FUZZY (INTELIGÊNCIA ARTIFICIAL)

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      CINTRA, M. E e CAMARGO, H. A e MONARD, Maria Carolina. Genetic generation of fuzzy systems with rule extraction using formal concept analysis. Information Sciences, v. 349-350, p. 199-215, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2016.02.026. Acesso em: 28 nov. 2025.
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      Cintra, M. E., Camargo, H. A., & Monard, M. C. (2016). Genetic generation of fuzzy systems with rule extraction using formal concept analysis. Information Sciences, 349-350, 199-215. doi:10.1016/j.ins.2016.02.026
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      Cintra ME, Camargo HA, Monard MC. Genetic generation of fuzzy systems with rule extraction using formal concept analysis [Internet]. Information Sciences. 2016 ; 349-350 199-215.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.ins.2016.02.026
    • Vancouver

      Cintra ME, Camargo HA, Monard MC. Genetic generation of fuzzy systems with rule extraction using formal concept analysis [Internet]. Information Sciences. 2016 ; 349-350 199-215.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.ins.2016.02.026
  • Source: Neurocomputing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      SPOLAÔR, Newton et al. A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, v. 180, p. 3-15, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2015.07.118. Acesso em: 28 nov. 2025.
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      Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. D. (2016). A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, 180, 3-15. doi:10.1016/j.neucom.2015.07.118
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      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
    • Vancouver

      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
  • Source: Journal of Intelligent and Robotic Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      CHERMAN, Everton Alvares et al. Lazy multi-label learning algorithms based on mutuality strategies. Journal of Intelligent and Robotic Systems, v. 80, p. S261-S276, 2015Tradução . . Disponível em: https://doi.org/10.1007/s10846-014-0144-4. Acesso em: 28 nov. 2025.
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      Cherman, E. A., Spolaôr, N., Valverde-Rebaza, J., & Monard, M. C. (2015). Lazy multi-label learning algorithms based on mutuality strategies. Journal of Intelligent and Robotic Systems, 80, S261-S276. doi:10.1007/s10846-014-0144-4
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      Cherman EA, Spolaôr N, Valverde-Rebaza J, Monard MC. Lazy multi-label learning algorithms based on mutuality strategies [Internet]. Journal of Intelligent and Robotic Systems. 2015 ; 80 S261-S276.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s10846-014-0144-4
    • Vancouver

      Cherman EA, Spolaôr N, Valverde-Rebaza J, Monard MC. Lazy multi-label learning algorithms based on mutuality strategies [Internet]. Journal of Intelligent and Robotic Systems. 2015 ; 80 S261-S276.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/s10846-014-0144-4
  • Source: Neurocomputing. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      BRAGA, Igor e MONARD, Maria Carolina. Improving the kernel regularized least squares method for small-sample regression. Neurocomputing, v. 163, p. 106-114, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2014.12.097. Acesso em: 28 nov. 2025.
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      Braga, I., & Monard, M. C. (2015). Improving the kernel regularized least squares method for small-sample regression. Neurocomputing, 163, 106-114. doi:10.1016/j.neucom.2014.12.097
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      Braga I, Monard MC. Improving the kernel regularized least squares method for small-sample regression [Internet]. Neurocomputing. 2015 ; 163 106-114.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.neucom.2014.12.097
    • Vancouver

      Braga I, Monard MC. Improving the kernel regularized least squares method for small-sample regression [Internet]. Neurocomputing. 2015 ; 163 106-114.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.neucom.2014.12.097
  • Source: Expert Systems with Applications. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      CHERMAN, Everton Alvares e METZ, Jean e MONARD, Maria Carolina. Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems with Applications, v. fe 2012, n. 2, p. 1647-1655, 2012Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2011.06.056. Acesso em: 28 nov. 2025.
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      Cherman, E. A., Metz, J., & Monard, M. C. (2012). Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems with Applications, fe 2012( 2), 1647-1655. doi:10.1016/j.eswa.2011.06.056
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      Cherman EA, Metz J, Monard MC. Incorporating label dependency into the binary relevance framework for multi-label classification [Internet]. Expert Systems with Applications. 2012 ; fe 2012( 2): 1647-1655.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.eswa.2011.06.056
    • Vancouver

      Cherman EA, Metz J, Monard MC. Incorporating label dependency into the binary relevance framework for multi-label classification [Internet]. Expert Systems with Applications. 2012 ; fe 2012( 2): 1647-1655.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.eswa.2011.06.056

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