Filtros : "MONARD, MARIA CAROLINA" "Inglês" Removido: "International Symposium on Knowledge Exploration in Life Science Informatics" Limpar

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  • Source: Evolving Systems. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO

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      CHERMAN, Everton Alvares et al. Multi-label active learning: key issues and a novel query strategy. Evolving Systems, v. 10, n. 1, p. 63-78, 2019Tradução . . Disponível em: https://doi.org/10.1007/s12530-017-9202-z. Acesso em: 27 nov. 2025.
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      Cherman, E. A., Papanikolaou, Y., Tsoumakas, G., & Monard, M. C. (2019). Multi-label active learning: key issues and a novel query strategy. Evolving Systems, 10( 1), 63-78. doi:10.1007/s12530-017-9202-z
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      Cherman EA, Papanikolaou Y, Tsoumakas G, Monard MC. Multi-label active learning: key issues and a novel query strategy [Internet]. Evolving Systems. 2019 ; 10( 1): 63-78.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s12530-017-9202-z
    • Vancouver

      Cherman EA, Papanikolaou Y, Tsoumakas G, Monard MC. Multi-label active learning: key issues and a novel query strategy [Internet]. Evolving Systems. 2019 ; 10( 1): 63-78.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s12530-017-9202-z
  • 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: 27 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. 27 ] 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. 27 ] Available from: https://doi.org/10.1016/j.ins.2016.02.026
  • Source: IFIP Advances in Information and Communication Technology. Conference titles: IFIP International Conference and Workshops on Artificial Intelligence Applications and Innovation - AIAI. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      CHERMAN, Everton Alvares e TSOUMAKAS, Grigorios e MONARD, Maria Carolina. Active learning algorithms for multi-label data. IFIP Advances in Information and Communication Technology. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-44944-9_23. Acesso em: 27 nov. 2025. , 2016
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      Cherman, E. A., Tsoumakas, G., & Monard, M. C. (2016). Active learning algorithms for multi-label data. IFIP Advances in Information and Communication Technology. Cham: Springer. doi:10.1007/978-3-319-44944-9_23
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      Cherman EA, Tsoumakas G, Monard MC. Active learning algorithms for multi-label data [Internet]. IFIP Advances in Information and Communication Technology. 2016 ; 475 267-279.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-319-44944-9_23
    • Vancouver

      Cherman EA, Tsoumakas G, Monard MC. Active learning algorithms for multi-label data [Internet]. IFIP Advances in Information and Communication Technology. 2016 ; 475 267-279.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-319-44944-9_23
  • 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: 27 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. 27 ] 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. 27 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
  • Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      BRAGA, Igor. Stochastic density ratio estimation and its application to feature selection. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf. Acesso em: 27 nov. 2025.
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      Braga, I. (2015). Stochastic density ratio estimation and its application to feature selection. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
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      Braga I. Stochastic density ratio estimation and its application to feature selection [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
    • Vancouver

      Braga I. Stochastic density ratio estimation and its application to feature selection [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-620.pdf
  • 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: 27 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. 27 ] 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. 27 ] Available from: https://doi.org/10.1007/s10846-014-0144-4
  • Source: Proceedings. Conference titles: IEEE International Conference on Fuzzy Systems - FUZZ-IEEE. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, FUZZY (INTELIGÊNCIA ARTIFICIAL)

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      CINTRA, Marcos E e MONARD, Maria Carolina e CAMARGO, Heloisa A. FCA-Based Rule Generator: a framework for the genetic generation of fuzzy classification systems using formal concept analysis. 2015, Anais.. Piscataway: IEEE, 2015. Disponível em: https://doi.org/10.1109/FUZZ-IEEE.2015.7337950. Acesso em: 27 nov. 2025.
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      Cintra, M. E., Monard, M. C., & Camargo, H. A. (2015). FCA-Based Rule Generator: a framework for the genetic generation of fuzzy classification systems using formal concept analysis. In Proceedings. Piscataway: IEEE. doi:10.1109/FUZZ-IEEE.2015.7337950
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      Cintra ME, Monard MC, Camargo HA. FCA-Based Rule Generator: a framework for the genetic generation of fuzzy classification systems using formal concept analysis [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/FUZZ-IEEE.2015.7337950
    • Vancouver

      Cintra ME, Monard MC, Camargo HA. FCA-Based Rule Generator: a framework for the genetic generation of fuzzy classification systems using formal concept analysis [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/FUZZ-IEEE.2015.7337950
  • 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: 27 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. 27 ] 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. 27 ] Available from: https://doi.org/10.1016/j.neucom.2014.12.097
  • Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      SPOLAÔR, Newton e MONARD, Maria Carolina e LEE, Huei Diana. Feature selection for multi-label learning. 2015, Anais.. Palo Alto: AAAI Press, 2015. Disponível em: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf. Acesso em: 27 nov. 2025.
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      Spolaôr, N., Monard, M. C., & Lee, H. D. (2015). Feature selection for multi-label learning. In Proceedings. Palo Alto: AAAI Press. Recuperado de http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
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      Spolaôr N, Monard MC, Lee HD. Feature selection for multi-label learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
    • Vancouver

      Spolaôr N, Monard MC, Lee HD. Feature selection for multi-label learning [Internet]. Proceedings. 2015 ;[citado 2025 nov. 27 ] Available from: http://ijcai.org/papers15/Papers/IJCAI15-648.pdf
  • Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      SPOLAÔR, Newton et al. Label construction for multi-label feature selection. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.52. Acesso em: 27 nov. 2025.
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      Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. D. (2014). Label construction for multi-label feature selection. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.52
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      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. Label construction for multi-label feature selection [Internet]. Proceedings. 2014 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2014.52
    • Vancouver

      Spolaôr N, Monard MC, Tsoumakas G, Lee HD. Label construction for multi-label feature selection [Internet]. Proceedings. 2014 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2014.52
  • Source: Electronic Notes in Theoretical Computer Science. Conference titles: Latin American Computing Conference - CLEI. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      TOMÁS, Jimena Torres et al. A framework to generate synthetic multi-label datasets. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. Disponível em: https://doi.org/10.1016/j.entcs.2014.01.025. Acesso em: 27 nov. 2025. , 2014
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      Tomás, J. T., Spolaôr, N., Cherman, E. A., & Monard, M. C. (2014). A framework to generate synthetic multi-label datasets. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. doi:10.1016/j.entcs.2014.01.025
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      Tomás JT, Spolaôr N, Cherman EA, Monard MC. A framework to generate synthetic multi-label datasets [Internet]. Electronic Notes in Theoretical Computer Science. 2014 ; fe 2014 155-176.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.entcs.2014.01.025
    • Vancouver

      Tomás JT, Spolaôr N, Cherman EA, Monard MC. A framework to generate synthetic multi-label datasets [Internet]. Electronic Notes in Theoretical Computer Science. 2014 ; fe 2014 155-176.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.entcs.2014.01.025
  • Source: Lecture Notes in Artifical Intelligence. Conference titles: Ibero-American Conference on Artificial Intelligence - IBERAMIA : Advances in Artificial Intelligence. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL

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      SPOLAÔR, Newton e MONARD, Maria Carolina. Evaluating ReliefF-based multi-label feature selection algorithm. Lecture Notes in Artifical Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-12027-0_16. Acesso em: 27 nov. 2025. , 2014
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      Spolaôr, N., & Monard, M. C. (2014). Evaluating ReliefF-based multi-label feature selection algorithm. Lecture Notes in Artifical Intelligence. Cham: Springer. doi:10.1007/978-3-319-12027-0_16
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      Spolaôr N, Monard MC. Evaluating ReliefF-based multi-label feature selection algorithm [Internet]. Lecture Notes in Artifical Intelligence. 2014 ; 8864 194-205.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-319-12027-0_16
    • Vancouver

      Spolaôr N, Monard MC. Evaluating ReliefF-based multi-label feature selection algorithm [Internet]. Lecture Notes in Artifical Intelligence. 2014 ; 8864 194-205.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-319-12027-0_16
  • Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SPOLAÔR, Newton et al. ReliefF for multi-label feature selection. 2013, Anais.. Los Alamitos: Conference Publishing Services, 2013. Disponível em: https://doi.org/10.1109/BRACIS.2013.10. Acesso em: 27 nov. 2025.
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      Spolaôr, N., Cherman, E. A., Monard, M. C., & Lee, H. D. (2013). ReliefF for multi-label feature selection. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2013.10
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      Spolaôr N, Cherman EA, Monard MC, Lee HD. ReliefF for multi-label feature selection [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2013.10
    • Vancouver

      Spolaôr N, Cherman EA, Monard MC, Lee HD. ReliefF for multi-label feature selection [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2013.10
  • Source: Lecture Notes in Artificial Intelligence. Conference titles: Mexican International Conference on Artificial Intelligence : Advances in Soft Computing and its Applications - MICAI. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      BRAGA, Igor et al. A note on parameter selection for support vector machines. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-45111-9_21. Acesso em: 27 nov. 2025. , 2013
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      Braga, I., Carmo, L. P. do, Benatti, C. C., & Monard, M. C. (2013). A note on parameter selection for support vector machines. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. doi:10.1007/978-3-642-45111-9_21
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      Braga I, Carmo LP do, Benatti CC, Monard MC. A note on parameter selection for support vector machines [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 8266 233-244.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-642-45111-9_21
    • Vancouver

      Braga I, Carmo LP do, Benatti CC, Monard MC. A note on parameter selection for support vector machines [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 8266 233-244.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-642-45111-9_21
  • Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      BRAGA, Igor e MONARD, Maria Carolina. Statistical and heuristic model selection in regularized least-squares. 2013, Anais.. Los Alamitos: Conference Publishing Services, 2013. Disponível em: https://doi.org/10.1109/BRACIS.2013.46. Acesso em: 27 nov. 2025.
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      Braga, I., & Monard, M. C. (2013). Statistical and heuristic model selection in regularized least-squares. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2013.46
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      Braga I, Monard MC. Statistical and heuristic model selection in regularized least-squares [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2013.46
    • Vancouver

      Braga I, Monard MC. Statistical and heuristic model selection in regularized least-squares [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRACIS.2013.46
  • Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      CINTRA, Marcos E e MONARD, Maria Carolina e CAMARGO, Heloisa A. On rule learning methods: a comparative analysis of classic and fuzzy approaches. Tradução . Berlin: Springer-Verlag, 2013. . . Acesso em: 27 nov. 2025.
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      Cintra, M. E., Monard, M. C., & Camargo, H. A. (2013). On rule learning methods: a comparative analysis of classic and fuzzy approaches. In . Berlin: Springer-Verlag.
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      Cintra ME, Monard MC, Camargo HA. On rule learning methods: a comparative analysis of classic and fuzzy approaches. Berlin: Springer-Verlag; 2013. [citado 2025 nov. 27 ]
    • Vancouver

      Cintra ME, Monard MC, Camargo HA. On rule learning methods: a comparative analysis of classic and fuzzy approaches. Berlin: Springer-Verlag; 2013. [citado 2025 nov. 27 ]
  • Source: Electronic Notes in Theoretical Computer Science. Conference titles: Latin American Conference in Informatics - CLEI. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SPOLAÔR, Newton et al. A comparison of multi-label feature selection methods using the problem transformation approach. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. Disponível em: https://doi.org/10.1016/j.entcs.2013.02.010. Acesso em: 27 nov. 2025. , 2013
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      Spolaôr, N., Cherman, E. A., Monard, M. C., & Lee, H. D. (2013). A comparison of multi-label feature selection methods using the problem transformation approach. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. doi:10.1016/j.entcs.2013.02.010
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      Spolaôr N, Cherman EA, Monard MC, Lee HD. A comparison of multi-label feature selection methods using the problem transformation approach [Internet]. Electronic Notes in Theoretical Computer Science. 2013 ; 292( 5): 135-151.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.entcs.2013.02.010
    • Vancouver

      Spolaôr N, Cherman EA, Monard MC, Lee HD. A comparison of multi-label feature selection methods using the problem transformation approach [Internet]. Electronic Notes in Theoretical Computer Science. 2013 ; 292( 5): 135-151.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.entcs.2013.02.010
  • Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SPOLAÔR, Newton et al. A systematic review on experimental multi-label learning. . São Carlos: ICMC-USP. Disponível em: http://www2.icmc.usp.br/~biblio/BIBLIOTECA/rel_tec/RT_392.pdf. Acesso em: 27 nov. 2025. , 2013
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      Spolaôr, N., Cherman, E. A., Metz, J., & Monard, M. C. (2013). A systematic review on experimental multi-label learning. São Carlos: ICMC-USP. Recuperado de http://www2.icmc.usp.br/~biblio/BIBLIOTECA/rel_tec/RT_392.pdf
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      Spolaôr N, Cherman EA, Metz J, Monard MC. A systematic review on experimental multi-label learning [Internet]. 2013 ;[citado 2025 nov. 27 ] Available from: http://www2.icmc.usp.br/~biblio/BIBLIOTECA/rel_tec/RT_392.pdf
    • Vancouver

      Spolaôr N, Cherman EA, Metz J, Monard MC. A systematic review on experimental multi-label learning [Internet]. 2013 ;[citado 2025 nov. 27 ] Available from: http://www2.icmc.usp.br/~biblio/BIBLIOTECA/rel_tec/RT_392.pdf
  • Source: Mathware & Soft Computing Magazine. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      CINTRA, Marcos E e MONARD, Maria Carolina e CAMARGO, Heloisa A. A fuzzy decision tree algorithm based on C4.5. Mathware & Soft Computing Magazine, v. 20, n. ju 2013, p. 56-62, 2013Tradução . . Disponível em: http://www.eusflat.org/msc/index.php. Acesso em: 27 nov. 2025.
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      Cintra, M. E., Monard, M. C., & Camargo, H. A. (2013). A fuzzy decision tree algorithm based on C4.5. Mathware & Soft Computing Magazine, 20( ju 2013), 56-62. Recuperado de http://www.eusflat.org/msc/index.php
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      Cintra ME, Monard MC, Camargo HA. A fuzzy decision tree algorithm based on C4.5 [Internet]. Mathware & Soft Computing Magazine. 2013 ; 20( ju 2013): 56-62.[citado 2025 nov. 27 ] Available from: http://www.eusflat.org/msc/index.php
    • Vancouver

      Cintra ME, Monard MC, Camargo HA. A fuzzy decision tree algorithm based on C4.5 [Internet]. Mathware & Soft Computing Magazine. 2013 ; 20( ju 2013): 56-62.[citado 2025 nov. 27 ] Available from: http://www.eusflat.org/msc/index.php
  • Source: Anais. Conference titles: Congresso Brasileiro de Sistemas Fuzzy : Recentes Avanços em Sistemas "Fuzzy" - CBSF. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      CINTRA, Marcos E e MONARD, Maria Carolina e CAMARGO, Heloisa A. FuzzyDT: a fuzzy decision tree algorithm based on C4.5. 2012, Anais.. Natal: SBMAC, 2012. Disponível em: http://www.lbd.dcc.ufmg.br/colecoes/cbsf/2012/0016.pdf. Acesso em: 27 nov. 2025.
    • APA

      Cintra, M. E., Monard, M. C., & Camargo, H. A. (2012). FuzzyDT: a fuzzy decision tree algorithm based on C4.5. In Anais. Natal: SBMAC. Recuperado de http://www.lbd.dcc.ufmg.br/colecoes/cbsf/2012/0016.pdf
    • NLM

      Cintra ME, Monard MC, Camargo HA. FuzzyDT: a fuzzy decision tree algorithm based on C4.5 [Internet]. Anais. 2012 ;[citado 2025 nov. 27 ] Available from: http://www.lbd.dcc.ufmg.br/colecoes/cbsf/2012/0016.pdf
    • Vancouver

      Cintra ME, Monard MC, Camargo HA. FuzzyDT: a fuzzy decision tree algorithm based on C4.5 [Internet]. Anais. 2012 ;[citado 2025 nov. 27 ] Available from: http://www.lbd.dcc.ufmg.br/colecoes/cbsf/2012/0016.pdf

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