Menu

Filtros : "Ghosh, Joydeep" Limpar

Filtros

Limitar por data

  • 1

1 - 11 (11 registros)

  • Artigo de periodico

    Combining clustering and active learning for the detection and learning of new image classes (2019)

    Coletta, Luiz Fernando Sommaggio ; Ponti, Moacir Antonelli ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep

    Fonte: Neurocomputing. Unidades: ICMC, EP

    Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      COLETTA, Luiz Fernando Sommaggio et al. Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing, v. 358, p. Se 2019, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2019.04.070. Acesso em: 27 nov. 2025.

    • APA

      Coletta, L. F. S., Ponti, M. A., Hruschka, E. R., Acharya, A., & Ghosh, J. (2019). Combining clustering and active learning for the detection and learning of new image classes. Neurocomputing, 358, Se 2019. doi:10.1016/j.neucom.2019.04.070

    • NLM

      Coletta LFS, Ponti MA, Hruschka ER, Acharya A, Ghosh J. Combining clustering and active learning for the detection and learning of new image classes [Internet]. Neurocomputing. 2019 ; 358 Se 2019.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.neucom.2019.04.070

    • Vancouver

      Coletta LFS, Ponti MA, Hruschka ER, Acharya A, Ghosh J. Combining clustering and active learning for the detection and learning of new image classes [Internet]. Neurocomputing. 2019 ; 358 Se 2019.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1016/j.neucom.2019.04.070

  • Artigo de periodico

    Evolving Gaussian mixture models with splitting and merging mutation operators (2016)

    Covões, Thiago Ferreira; Hruschka, Eduardo Raul ; Ghosh, Joydeep

    Fonte: Evolutionary Computation. Unidade: ICMC

    Assuntos: COMPUTAÇÃO EVOLUTIVA, ALGORITMOS GENÉTICOS

    Acesso à fonteDOIComo citar
    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. Evolving Gaussian mixture models with splitting and merging mutation operators. Evolutionary Computation, v. 24, n. 2, p. 293-317, 2016Tradução . . Disponível em: https://doi.org/10.1162/EVCO_a_00152. Acesso em: 27 nov. 2025.

    • APA

      Covões, T. F., Hruschka, E. R., & Ghosh, J. (2016). Evolving Gaussian mixture models with splitting and merging mutation operators. Evolutionary Computation, 24( 2), 293-317. doi:10.1162/EVCO_a_00152

    • NLM

      Covões TF, Hruschka ER, Ghosh J. Evolving Gaussian mixture models with splitting and merging mutation operators [Internet]. Evolutionary Computation. 2016 ; 24( 2): 293-317.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1162/EVCO_a_00152

    • Vancouver

      Covões TF, Hruschka ER, Ghosh J. Evolving Gaussian mixture models with splitting and merging mutation operators [Internet]. Evolutionary Computation. 2016 ; 24( 2): 293-317.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1162/EVCO_a_00152

  • Artigo de periodico

    Using metaheuristics to optimize the combination of classifier and cluster ensembles (2015)

    Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep

    Fonte: Integrated Computer-Aided Engineering. Unidade: ICMC

    Assuntos: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      COLETTA, Luiz Fernando Sommaggio et al. Using metaheuristics to optimize the combination of classifier and cluster ensembles. Integrated Computer-Aided Engineering, v. 22, n. 3, p. 229-242, 2015Tradução . . Disponível em: https://doi.org/10.3233/ICA-150485. Acesso em: 27 nov. 2025.

    • APA

      Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2015). Using metaheuristics to optimize the combination of classifier and cluster ensembles. Integrated Computer-Aided Engineering, 22( 3), 229-242. doi:10.3233/ICA-150485

    • NLM

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Using metaheuristics to optimize the combination of classifier and cluster ensembles [Internet]. Integrated Computer-Aided Engineering. 2015 ; 22( 3): 229-242.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3233/ICA-150485

    • Vancouver

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Using metaheuristics to optimize the combination of classifier and cluster ensembles [Internet]. Integrated Computer-Aided Engineering. 2015 ; 22( 3): 229-242.[citado 2025 nov. 27 ] Available from: https://doi.org/10.3233/ICA-150485

  • Artigo de periodico

    A differential evolution algorithm to optimise the combination of classifier and cluster ensembles (2015)

    Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep

    Fonte: International Journal of Bio-Inspired Computation. Unidade: ICMC

    Assuntos: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      COLETTA, Luiz Fernando Sommaggio et al. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles. International Journal of Bio-Inspired Computation, v. 7, n. 2, p. 111-124, 2015Tradução . . Disponível em: https://doi.org/10.1504/IJBIC.2015.069288. Acesso em: 27 nov. 2025.

    • APA

      Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2015). A differential evolution algorithm to optimise the combination of classifier and cluster ensembles. International Journal of Bio-Inspired Computation, 7( 2), 111-124. doi:10.1504/IJBIC.2015.069288

    • NLM

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles [Internet]. International Journal of Bio-Inspired Computation. 2015 ; 7( 2): 111-124.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1504/IJBIC.2015.069288

    • Vancouver

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles [Internet]. International Journal of Bio-Inspired Computation. 2015 ; 7( 2): 111-124.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1504/IJBIC.2015.069288

  • Artigo de periodico

    An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning (2014)

    Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Acharyya, Sreangsu

    Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

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

    Acesso à fonteDOIComo citar
    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: 27 nov. 2025.

    • 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 2025 nov. 27 ] 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 2025 nov. 27 ] Available from: https://doi.org/10.1145/2601435

  • Artigo de periodico

    Competitive learning with pairwise constraints (2013)

    Covões, Thiago F; Hruschka, Eduardo Raul ; Ghosh, Joydeep

    Fonte: IEEE Transactions on Neural Networks and Learning Systems. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      COVÕES, Thiago F e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. Competitive learning with pairwise constraints. IEEE Transactions on Neural Networks and Learning Systems, v. 24, n. ja 2013, p. 164-169, 2013Tradução . . Disponível em: https://doi.org/10.1109/TNNLS.2012.2227064. Acesso em: 27 nov. 2025.

    • APA

      Covões, T. F., Hruschka, E. R., & Ghosh, J. (2013). Competitive learning with pairwise constraints. IEEE Transactions on Neural Networks and Learning Systems, 24( ja 2013), 164-169. doi:10.1109/TNNLS.2012.2227064

    • NLM

      Covões TF, Hruschka ER, Ghosh J. Competitive learning with pairwise constraints [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2013 ; 24( ja 2013): 164-169.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/TNNLS.2012.2227064

    • Vancouver

      Covões TF, Hruschka ER, Ghosh J. Competitive learning with pairwise constraints [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2013 ; 24( ja 2013): 164-169.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/TNNLS.2012.2227064

  • Trabalho de evento

    Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles (2013)

    Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep

    Fonte: Proceedings. Nome do evento: BRICS Countries Congress on Computational Intelligence - BRICS-CCI. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      COLETTA, Luiz Fernando Sommaggio et al. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles. 2013, Anais.. Piscataway: IEEE, 2013. Disponível em: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86. Acesso em: 27 nov. 2025.

    • APA

      Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2013). Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles. In Proceedings. Piscataway: IEEE. doi:10.1109/BRICS-CCI-CBIC.2013.86

    • NLM

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86

    • Vancouver

      Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles [Internet]. Proceedings. 2013 ;[citado 2025 nov. 27 ] Available from: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86

  • Artigo de periodico

    A study of K-means-based algorithms for constrained clustering (2013)

    Covões, Thiago Ferreira; Hruschka, Eduardo Raul ; Ghosh, Joydeep

    Fonte: Intelligent Data Analysis. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIComo citar
    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: 27 nov. 2025.

    • 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 2025 nov. 27 ] 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 2025 nov. 27 ] Available from: https://doi.org/10.3233/IDA-130590

  • Trabalho de evento

    Probabilistic combination of classifier and cluster ensembles for non-transductive learning (2013)

    Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Sarwar, Badrul; Ruvini, Jean-David

    Fonte: Proceedings. Nome do evento: SIAM International Conference on Data Mining. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteAcesso à fonteDOIComo citar
    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: 27 nov. 2025.

    • 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 2025 nov. 27 ] 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 2025 nov. 27 ] Available from: https://doi.org/10.1137/1.9781611972832.32

  • Trabalho de evento-anais periodico

    Transfer learning with cluster ensembles (2012)

    Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Acharyya, Sreangsu

    Fonte: JMLR: Workshop and Conference Proceedings. Nome do evento: International Conference on Machine Learning - ICML. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteComo citar
    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: 27 nov. 2025. , 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 2025 nov. 27 ] 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 2025 nov. 27 ] Available from: http://jmlr.csail.mit.edu/proceedings/papers/v27/

  • Trabalho de evento

    A privacy-aware Bayesian approach for combining classifier and cluster ensembles (2011)

    Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep

    Fonte: Proceedings. Nome do evento: IEEE International Conference on Privacy, Security, Risk, and Trust - PASSAT. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIComo citar
    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: 27 nov. 2025.

    • 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 2025 nov. 27 ] 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 2025 nov. 27 ] Available from: https://doi.org/10.1109/PASSAT/SocialCom.2011.172
  • 1

1 - 11 (11 registros)

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo     2012 - 2025