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Filtros : "HRUSCHKA, EDUARDO RAUL" "Holanda" Limpar

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1 - 13 (13 records)

  • 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

    Source: Neurocomputing. Unidades: ICMC, EP

    Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE IMAGEM

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      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: 30 set. 2024.

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      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 2024 set. 30 ] 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 2024 set. 30 ] Available from: https://doi.org/10.1016/j.neucom.2019.04.070

  • Artigo de periodico

    Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: a comparison of meta-features (2016)

    Kanda, Jorge; Carvalho, André Carlos Ponce de Leon Ferreira de ; Hruschka, Eduardo Raul ; Soares, Carlos; Brazdil, Pavel

    Source: Neurocomputing. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, HEURÍSTICA

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      KANDA, Jorge et al. Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: a comparison of meta-features. Neurocomputing, v. 205, p. Se 2016, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2016.04.027. Acesso em: 30 set. 2024.

    • APA

      Kanda, J., Carvalho, A. C. P. de L. F. de, Hruschka, E. R., Soares, C., & Brazdil, P. (2016). Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: a comparison of meta-features. Neurocomputing, 205, Se 2016. doi:10.1016/j.neucom.2016.04.027

    • NLM

      Kanda J, Carvalho ACP de LF de, Hruschka ER, Soares C, Brazdil P. Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: a comparison of meta-features [Internet]. Neurocomputing. 2016 ; 205 Se 2016.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.neucom.2016.04.027

    • Vancouver

      Kanda J, Carvalho ACP de LF de, Hruschka ER, Soares C, Brazdil P. Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: a comparison of meta-features [Internet]. Neurocomputing. 2016 ; 205 Se 2016.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.neucom.2016.04.027

  • 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

    Source: Integrated Computer-Aided Engineering. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS

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      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: 30 set. 2024.

    • 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 2024 set. 30 ] 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 2024 set. 30 ] Available from: https://doi.org/10.3233/ICA-150485

  • Artigo de periodico

    Simultaneous co-clustering and learning to address the cold start problem in recommender systems (2015)

    Pereira, Andre Luiz Vizine; Hruschka, Eduardo Raul

    Source: Knowledge-Based Systems. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS

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      PEREIRA, Andre Luiz Vizine e HRUSCHKA, Eduardo Raul. Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, v. 82, p. 11-19, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2015.02.016. Acesso em: 30 set. 2024.

    • APA

      Pereira, A. L. V., & Hruschka, E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82, 11-19. doi:10.1016/j.knosys.2015.02.016

    • NLM

      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016

    • Vancouver

      Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016

  • Artigo de periodico

    Interactive textual feature selection for consensus clustering (2015)

    Corrêa, Geraldo N; Marcacini, Ricardo Marcondes ; Hruschka, Eduardo Raul ; Rezende, Solange Oliveira

    Source: Pattern Recognition Letters. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS

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      CORRÊA, Geraldo N et al. Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, v. 52, n. ja 2015, p. 25-31, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.patrec.2014.09.008. Acesso em: 30 set. 2024.

    • APA

      Corrêa, G. N., Marcacini, R. M., Hruschka, E. R., & Rezende, S. O. (2015). Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, 52( ja 2015), 25-31. doi:10.1016/j.patrec.2014.09.008

    • NLM

      Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008

    • Vancouver

      Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008

  • Artigo de periodico

    Tweet sentiment analysis with classifier ensembles (2014)

    Silva, Nádia Felix Felipe da; Hruschka, Eduardo Raul ; Hruschka Junior, Estevam Rafael

    Source: Decision Support Systems. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SILVA, Nádia Felix Felipe da e HRUSCHKA, Eduardo Raul e HRUSCHKA JUNIOR, Estevam Rafael. Tweet sentiment analysis with classifier ensembles. Decision Support Systems, v. 66, p. 170-179, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.dss.2014.07.003. Acesso em: 30 set. 2024.

    • APA

      Silva, N. F. F. da, Hruschka, E. R., & Hruschka Junior, E. R. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170-179. doi:10.1016/j.dss.2014.07.003

    • NLM

      Silva NFF da, Hruschka ER, Hruschka Junior ER. Tweet sentiment analysis with classifier ensembles [Internet]. Decision Support Systems. 2014 ; 66 170-179.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.dss.2014.07.003

    • Vancouver

      Silva NFF da, Hruschka ER, Hruschka Junior ER. Tweet sentiment analysis with classifier ensembles [Internet]. Decision Support Systems. 2014 ; 66 170-179.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.dss.2014.07.003

  • Artigo de periodico

    An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks (2013)

    Silva, Jonathan de Andrade; Hruschka, Eduardo Raul

    Source: Data & Knowledge Engineering. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks. Data & Knowledge Engineering, v. 84, p. 47-58, 2013Tradução . . Disponível em: https://doi.org/10.1016/j.datak.2012.12.006. Acesso em: 30 set. 2024.

    • APA

      Silva, J. de A., & Hruschka, E. R. (2013). An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks. Data & Knowledge Engineering, 84, 47-58. doi:10.1016/j.datak.2012.12.006

    • NLM

      Silva J de A, Hruschka ER. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks [Internet]. Data & Knowledge Engineering. 2013 ; 84 47-58.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.datak.2012.12.006

    • Vancouver

      Silva J de A, Hruschka ER. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks [Internet]. Data & Knowledge Engineering. 2013 ; 84 47-58.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.datak.2012.12.006

  • Artigo de periodico

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

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

    Source: Intelligent Data Analysis. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      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: 30 set. 2024.

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      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 set. 30 ] 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 set. 30 ] Available from: https://doi.org/10.3233/IDA-130590

  • Artigo de periodico

    Selection of algorithms to solve traveling salesman problems using meta-learning (2011)

    Kanda, Jorge Yoshio; Carvalho, André Carlos Ponce de Leon Ferreira de ; Hruschka, Eduardo Raul ; Soares, Carlos

    Source: International Journal of Hybrid Intelligent Systems. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      KANDA, Jorge Yoshio et al. Selection of algorithms to solve traveling salesman problems using meta-learning. International Journal of Hybrid Intelligent Systems, v. 8, n. 3, p. 117-128, 2011Tradução . . Disponível em: https://doi.org/10.3233/HIS-2011-0133. Acesso em: 30 set. 2024.

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      Kanda, J. Y., Carvalho, A. C. P. de L. F. de, Hruschka, E. R., & Soares, C. (2011). Selection of algorithms to solve traveling salesman problems using meta-learning. International Journal of Hybrid Intelligent Systems, 8( 3), 117-128. doi:10.3233/HIS-2011-0133

    • NLM

      Kanda JY, Carvalho ACP de LF de, Hruschka ER, Soares C. Selection of algorithms to solve traveling salesman problems using meta-learning [Internet]. International Journal of Hybrid Intelligent Systems. 2011 ; 8( 3): 117-128.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/HIS-2011-0133

    • Vancouver

      Kanda JY, Carvalho ACP de LF de, Hruschka ER, Soares C. Selection of algorithms to solve traveling salesman problems using meta-learning [Internet]. International Journal of Hybrid Intelligent Systems. 2011 ; 8( 3): 117-128.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/HIS-2011-0133

  • Artigo de periodico

    Bayesian network classifiers: beyond classification accuracy (2011)

    Santos, Edimilson Batista dos; Hruschka Junior, Estevam Rafael ; Hruschka, Eduardo Raul ; Ebecken, Nelson Francisco Favilla

    Source: Intelligent Data Analysis : an International Journal. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      SANTOS, Edimilson Batista dos et al. Bayesian network classifiers: beyond classification accuracy. Intelligent Data Analysis : an International Journal, v. 15, n. 3, p. 279-298, 2011Tradução . . Disponível em: https://doi.org/10.3233/IDA-2010-0468. Acesso em: 30 set. 2024.

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      Santos, E. B. dos, Hruschka Junior, E. R., Hruschka, E. R., & Ebecken, N. F. F. (2011). Bayesian network classifiers: beyond classification accuracy. Intelligent Data Analysis : an International Journal, 15( 3), 279-298. doi:10.3233/IDA-2010-0468

    • NLM

      Santos EB dos, Hruschka Junior ER, Hruschka ER, Ebecken NFF. Bayesian network classifiers: beyond classification accuracy [Internet]. Intelligent Data Analysis : an International Journal. 2011 ;15( 3): 279-298.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/IDA-2010-0468

    • Vancouver

      Santos EB dos, Hruschka Junior ER, Hruschka ER, Ebecken NFF. Bayesian network classifiers: beyond classification accuracy [Internet]. Intelligent Data Analysis : an International Journal. 2011 ;15( 3): 279-298.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/IDA-2010-0468

  • Artigo de periodico

    A Bayesian imputation method for a clustering genetic algorithm (2011)

    Hruschka Junior, Estevam Rafael ; Hruschka, Eduardo Raul ; Ebecken, Nelson Francisco Favilla

    Source: Journal of Computational Methods in Sciences and Engineering. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      HRUSCHKA JUNIOR, Estevam Rafael e HRUSCHKA, Eduardo Raul e EBECKEN, Nelson Francisco Favilla. A Bayesian imputation method for a clustering genetic algorithm. Journal of Computational Methods in Sciences and Engineering, v. 11, n. 4, p. 173-183, 2011Tradução . . Disponível em: https://doi.org/10.3233/JCM-2011-0362. Acesso em: 30 set. 2024.

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      Hruschka Junior, E. R., Hruschka, E. R., & Ebecken, N. F. F. (2011). A Bayesian imputation method for a clustering genetic algorithm. Journal of Computational Methods in Sciences and Engineering, 11( 4), 173-183. doi:10.3233/JCM-2011-0362

    • NLM

      Hruschka Junior ER, Hruschka ER, Ebecken NFF. A Bayesian imputation method for a clustering genetic algorithm [Internet]. Journal of Computational Methods in Sciences and Engineering. 2011 ; 11( 4): 173-183.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/JCM-2011-0362

    • Vancouver

      Hruschka Junior ER, Hruschka ER, Ebecken NFF. A Bayesian imputation method for a clustering genetic algorithm [Internet]. Journal of Computational Methods in Sciences and Engineering. 2011 ; 11( 4): 173-183.[citado 2024 set. 30 ] Available from: https://doi.org/10.3233/JCM-2011-0362

  • Artigo de periodico

    Efficiency issues of evolutionary k-means (2011)

    Naldi, Murilo Coelho; Campello, Ricardo José Gabrielli Barreto; Hruschka, Eduardo Raul ; Carvalho, André Carlos Ponce de Leon Ferreira de

    Source: Applied Soft Computing. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      NALDI, Murilo Coelho et al. Efficiency issues of evolutionary k-means. Applied Soft Computing, v. 11, n. 2, p. 1938-1952, 2011Tradução . . Disponível em: https://doi.org/10.1016/j.asoc.2010.06.010. Acesso em: 30 set. 2024.

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      Naldi, M. C., Campello, R. J. G. B., Hruschka, E. R., & Carvalho, A. C. P. de L. F. de. (2011). Efficiency issues of evolutionary k-means. Applied Soft Computing, 11( 2), 1938-1952. doi:10.1016/j.asoc.2010.06.010

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      Naldi MC, Campello RJGB, Hruschka ER, Carvalho ACP de LF de. Efficiency issues of evolutionary k-means [Internet]. Applied Soft Computing. 2011 ; 11( 2): 1938-1952.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.asoc.2010.06.010

    • Vancouver

      Naldi MC, Campello RJGB, Hruschka ER, Carvalho ACP de LF de. Efficiency issues of evolutionary k-means [Internet]. Applied Soft Computing. 2011 ; 11( 2): 1938-1952.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.asoc.2010.06.010

  • Artigo de periodico

    Towards improving cluster-based feature selection with a simplified silhouette filter (2011)

    Covões, Thiago Ferreira; Hruschka, Eduardo Raul

    Source: Information Sciences. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul. Towards improving cluster-based feature selection with a simplified silhouette filter. Information Sciences, v. 181, n. 18, p. 3766-3782, 2011Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2011.04.050. Acesso em: 30 set. 2024.

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      Covões, T. F., & Hruschka, E. R. (2011). Towards improving cluster-based feature selection with a simplified silhouette filter. Information Sciences, 181( 18), 3766-3782. doi:10.1016/j.ins.2011.04.050

    • NLM

      Covões TF, Hruschka ER. Towards improving cluster-based feature selection with a simplified silhouette filter [Internet]. Information Sciences. 2011 ; 181( 18): 3766-3782.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.ins.2011.04.050

    • Vancouver

      Covões TF, Hruschka ER. Towards improving cluster-based feature selection with a simplified silhouette filter [Internet]. Information Sciences. 2011 ; 181( 18): 3766-3782.[citado 2024 set. 30 ] Available from: https://doi.org/10.1016/j.ins.2011.04.050
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