Filtros : "INTELIGÊNCIA ARTIFICIAL" "University of Texas (UT)" Limpar

Filtros



Refine with date range


  • Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS, VISÃO COMPUTACIONAL

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      ACHARYA, Ayan et al. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, v. 9, n. 1, p. 1:1-1:35, 2014Tradução . . Disponível em: https://doi.org/10.1145/2601435. Acesso em: 31 maio 2024.
    • APA

      Acharya, A., Hruschka, E. R., Ghosh, J., & Acharyya, S. (2014). An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, 9( 1), 1:1-1:35. doi:10.1145/2601435
    • NLM

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 maio 31 ] Available from: https://doi.org/10.1145/2601435
    • Vancouver

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 maio 31 ] Available from: https://doi.org/10.1145/2601435
  • Source: Intelligent Data Analysis. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, v. 17, n. 3, p. 485-505, 2013Tradução . . Disponível em: https://doi.org/10.3233/IDA-130590. Acesso em: 31 maio 2024.
    • APA

      Covões, T. F., Hruschka, E. R., & Ghosh, J. (2013). A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, 17( 3), 485-505. doi:10.3233/IDA-130590
    • NLM

      Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2024 maio 31 ] Available from: https://doi.org/10.3233/IDA-130590
    • Vancouver

      Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2024 maio 31 ] Available from: https://doi.org/10.3233/IDA-130590
  • Source: Proceedings. Conference titles: SIAM International Conference on Data Mining. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      ACHARYA, Ayan et al. Probabilistic combination of classifier and cluster ensembles for non-transductive learning. 2013, Anais.. Philadelphia: SIAM, 2013. Disponível em: https://doi.org/10.1137/1.9781611972832.32. Acesso em: 31 maio 2024.
    • APA

      Acharya, A., Hruschka, E. R., Ghosh, J., Sarwar, B., & Ruvini, J. -D. (2013). Probabilistic combination of classifier and cluster ensembles for non-transductive learning. In Proceedings. Philadelphia: SIAM. doi:10.1137/1.9781611972832.32
    • NLM

      Acharya A, Hruschka ER, Ghosh J, Sarwar B, Ruvini J-D. Probabilistic combination of classifier and cluster ensembles for non-transductive learning [Internet]. Proceedings. 2013 ;[citado 2024 maio 31 ] Available from: https://doi.org/10.1137/1.9781611972832.32
    • Vancouver

      Acharya A, Hruschka ER, Ghosh J, Sarwar B, Ruvini J-D. Probabilistic combination of classifier and cluster ensembles for non-transductive learning [Internet]. Proceedings. 2013 ;[citado 2024 maio 31 ] Available from: https://doi.org/10.1137/1.9781611972832.32
  • Source: JMLR: Workshop and Conference Proceedings. Conference titles: International Conference on Machine Learning - ICML. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      ACHARYA, Ayan et al. Transfer learning with cluster ensembles. JMLR: Workshop and Conference Proceedings. Brookline: Microtome Publishing. Disponível em: http://jmlr.csail.mit.edu/proceedings/papers/v27/. Acesso em: 31 maio 2024. , 2012
    • APA

      Acharya, A., Hruschka, E. R., Ghosh, J., & Acharyya, S. (2012). Transfer learning with cluster ensembles. JMLR: Workshop and Conference Proceedings. Brookline: Microtome Publishing. Recuperado de http://jmlr.csail.mit.edu/proceedings/papers/v27/
    • NLM

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. Transfer learning with cluster ensembles [Internet]. JMLR: Workshop and Conference Proceedings. 2012 ; 27 123-133.[citado 2024 maio 31 ] Available from: http://jmlr.csail.mit.edu/proceedings/papers/v27/
    • Vancouver

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. Transfer learning with cluster ensembles [Internet]. JMLR: Workshop and Conference Proceedings. 2012 ; 27 123-133.[citado 2024 maio 31 ] Available from: http://jmlr.csail.mit.edu/proceedings/papers/v27/
  • Source: Proceedings. Conference titles: IEEE International Conference on Privacy, Security, Risk, and Trust - PASSAT. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
    • ABNT

      ACHARYA, Ayan e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. A privacy-aware Bayesian approach for combining classifier and cluster ensembles. 2011, Anais.. Los Alamintos: IEEE Conference Publishing Services, 2011. Disponível em: https://doi.org/10.1109/PASSAT/SocialCom.2011.172. Acesso em: 31 maio 2024.
    • APA

      Acharya, A., Hruschka, E. R., & Ghosh, J. (2011). A privacy-aware Bayesian approach for combining classifier and cluster ensembles. In Proceedings. Los Alamintos: IEEE Conference Publishing Services. doi:10.1109/PASSAT/SocialCom.2011.172
    • NLM

      Acharya A, Hruschka ER, Ghosh J. A privacy-aware Bayesian approach for combining classifier and cluster ensembles [Internet]. Proceedings. 2011 ;[citado 2024 maio 31 ] Available from: https://doi.org/10.1109/PASSAT/SocialCom.2011.172
    • Vancouver

      Acharya A, Hruschka ER, Ghosh J. A privacy-aware Bayesian approach for combining classifier and cluster ensembles [Internet]. Proceedings. 2011 ;[citado 2024 maio 31 ] Available from: https://doi.org/10.1109/PASSAT/SocialCom.2011.172

Digital Library of Intellectual Production of Universidade de São Paulo     2012 - 2024