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Filtros : "Data Mining and Knowledge Discovery" "BATISTA, GUSTAVO ENRIQUE DE ALMEIDA PRADO ALVES" Limpar

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  • Artigo de periodico

    Challenges in benchmarking stream learning algorithms with real-world data (2020)

    Souza, Vinícius Mourão Alves de ; Reis, Denis Moreira dos ; Maletzke, André Gustavo ; Batista, Gustavo Enrique de Almeida Prado Alves

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS, ALGORITMOS ÚTEIS E ESPECÍFICOS, BENCHMARKS

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

    • ABNT

      SOUZA, Vinícius Mourão Alves de et al. Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, v. No 2020, n. 6, p. 1805-1858, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10618-020-00698-5. Acesso em: 12 nov. 2025.

    • APA

      Souza, V. M. A. de, Reis, D. M. dos, Maletzke, A. G., & Batista, G. E. de A. P. A. (2020). Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, No 2020( 6), 1805-1858. doi:10.1007/s10618-020-00698-5

    • NLM

      Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-020-00698-5

    • Vancouver

      Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-020-00698-5

  • Artigo de periodico

    Speeding up similarity search under dynamic time warping by pruning unpromising alignments (2018)

    Silva, Diego Furtado ; Giusti, Rafael; Keogh, Eamonn; Batista, Gustavo Enrique de Almeida Prado Alves

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES

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

    • ABNT

      SILVA, Diego Furtado et al. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, v. 32, n. 4, p. 988-1016, 2018Tradução . . Disponível em: https://doi.org/10.1007/s10618-018-0557-y. Acesso em: 12 nov. 2025.

    • APA

      Silva, D. F., Giusti, R., Keogh, E., & Batista, G. E. de A. P. A. (2018). Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, 32( 4), 988-1016. doi:10.1007/s10618-018-0557-y

    • NLM

      Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-018-0557-y

    • Vancouver

      Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-018-0557-y

  • Artigo de periodico

    CID: an efficient complexity-invariant distance for time series (2014)

    Batista, Gustavo Enrique de Almeida Prado Alves; Keogh, Eamonn J; Tataw, Oben Moses; Souza, Vinícius M. A. de

    Source: Data Mining and Knowledge Discovery. 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

      BATISTA, Gustavo Enrique de Almeida Prado Alves et al. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, v. 28, n. 3, p. 634-669, 2014Tradução . . Disponível em: https://doi.org/10.1007/s10618-013-0312-3. Acesso em: 12 nov. 2025.

    • APA

      Batista, G. E. de A. P. A., Keogh, E. J., Tataw, O. M., & Souza, V. M. A. de. (2014). CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28( 3), 634-669. doi:10.1007/s10618-013-0312-3

    • NLM

      Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-013-0312-3

    • Vancouver

      Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
  • 1

1 - 3 (3 records)

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