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Artigo de periodico
An open-source tool for classification models in resource-constrained hardware (2022)
Silva, Lucas Tsutsui da ; Souza, Vinícius Mourão Alves de ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: IEEE Sensors Journal. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, SISTEMAS EMBUTIDOS, RECONHECIMENTO DE PADRÕES, SENSOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Lucas Tsutsui da e SOUZA, Vinícius Mourão Alves de e BATISTA, Gustavo Enrique de Almeida Prado Alves. An open-source tool for classification models in resource-constrained hardware. IEEE Sensors Journal, v. 22, n. Ja 2022, p. 544-554, 2022Tradução . . Disponível em: https://doi.org/10.1109/JSEN.2021.3128130. Acesso em: 04 nov. 2024.
APA
Silva, L. T. da, Souza, V. M. A. de, & Batista, G. E. de A. P. A. (2022). An open-source tool for classification models in resource-constrained hardware. IEEE Sensors Journal, 22( Ja 2022), 544-554. doi:10.1109/JSEN.2021.3128130
NLM
Silva LT da, Souza VMA de, Batista GE de APA. An open-source tool for classification models in resource-constrained hardware [Internet]. IEEE Sensors Journal. 2022 ; 22( Ja 2022): 544-554.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/JSEN.2021.3128130
Vancouver
Silva LT da, Souza VMA de, Batista GE de APA. An open-source tool for classification models in resource-constrained hardware [Internet]. IEEE Sensors Journal. 2022 ; 22( Ja 2022): 544-554.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/JSEN.2021.3128130
Artigo de periodico
Fast similarity matrix profile for music analysis and exploration (2019)
Silva, Diego Furtado ; Yeh, Chin-Chin M; Zhu, Yan; Batista, Gustavo Enrique de Almeida Prado Alves; Keogh, Eamonn
Source: IEEE Transactions on Multimedia. Unidade: ICMC
Subjects: RECUPERAÇÃO DA INFORMAÇÃO, MINERAÇÃO DE DADOS, MÚSICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Diego Furtado et al. Fast similarity matrix profile for music analysis and exploration. IEEE Transactions on Multimedia, v. 21, n. Ja 2019, p. 29-38, 2019Tradução . . Disponível em: https://doi.org/10.1109/TMM.2018.2849563. Acesso em: 04 nov. 2024.
APA
Silva, D. F., Yeh, C. -C. M., Zhu, Y., Batista, G. E. de A. P. A., & Keogh, E. (2019). Fast similarity matrix profile for music analysis and exploration. IEEE Transactions on Multimedia, 21( Ja 2019), 29-38. doi:10.1109/TMM.2018.2849563
NLM
Silva DF, Yeh C-CM, Zhu Y, Batista GE de APA, Keogh E. Fast similarity matrix profile for music analysis and exploration [Internet]. IEEE Transactions on Multimedia. 2019 ; 21( Ja 2019): 29-38.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/TMM.2018.2849563
Vancouver
Silva DF, Yeh C-CM, Zhu Y, Batista GE de APA, Keogh E. Fast similarity matrix profile for music analysis and exploration [Internet]. IEEE Transactions on Multimedia. 2019 ; 21( Ja 2019): 29-38.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1109/TMM.2018.2849563
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
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: 04 nov. 2024.
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 2024 nov. 04 ] 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 2024 nov. 04 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
Artigo de periodico
Combining instance selection and self-training to improve data stream quantification (2018)
Maletzke, André Gustavo; Reis, Denis Moreira dos; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Journal of the Brazilian Computer Society. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALETZKE, André Gustavo e REIS, Denis Moreira dos e BATISTA, Gustavo Enrique de Almeida Prado Alves. Combining instance selection and self-training to improve data stream quantification. Journal of the Brazilian Computer Society, v. 24, p. 1-17, 2018Tradução . . Disponível em: https://doi.org/10.1186/s13173-018-0076-0. Acesso em: 04 nov. 2024.
APA
Maletzke, A. G., Reis, D. M. dos, & Batista, G. E. de A. P. A. (2018). Combining instance selection and self-training to improve data stream quantification. Journal of the Brazilian Computer Society, 24, 1-17. doi:10.1186/s13173-018-0076-0
NLM
Maletzke AG, Reis DM dos, Batista GE de APA. Combining instance selection and self-training to improve data stream quantification [Internet]. Journal of the Brazilian Computer Society. 2018 ; 24 1-17.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1186/s13173-018-0076-0
Vancouver
Maletzke AG, Reis DM dos, Batista GE de APA. Combining instance selection and self-training to improve data stream quantification [Internet]. Journal of the Brazilian Computer Society. 2018 ; 24 1-17.[citado 2024 nov. 04 ] Available from: https://doi.org/10.1186/s13173-018-0076-0
Artigo de periodico
Unsupervised active learning techniques for labeling training sets: an experimental evaluation on sequential data (2017)
Souza, Vinícius M. A; Rossi, Rafael G; Batista, Gustavo Enrique de Almeida Prado Alves; Rezende, Solange Oliveira
Source: Intelligent Data Analysis. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ANÁLISE DE SÉRIES TEMPORAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Vinícius M. A et al. Unsupervised active learning techniques for labeling training sets: an experimental evaluation on sequential data. Intelligent Data Analysis, v. 21, n. 5, p. 1061-1095, 2017Tradução . . Disponível em: https://doi.org/10.3233/IDA-163075. Acesso em: 04 nov. 2024.
APA
Souza, V. M. A., Rossi, R. G., Batista, G. E. de A. P. A., & Rezende, S. O. (2017). Unsupervised active learning techniques for labeling training sets: an experimental evaluation on sequential data. Intelligent Data Analysis, 21( 5), 1061-1095. doi:10.3233/IDA-163075
NLM
Souza VMA, Rossi RG, Batista GE de APA, Rezende SO. Unsupervised active learning techniques for labeling training sets: an experimental evaluation on sequential data [Internet]. Intelligent Data Analysis. 2017 ; 21( 5): 1061-1095.[citado 2024 nov. 04 ] Available from: https://doi.org/10.3233/IDA-163075
Vancouver
Souza VMA, Rossi RG, Batista GE de APA, Rezende SO. Unsupervised active learning techniques for labeling training sets: an experimental evaluation on sequential data [Internet]. Intelligent Data Analysis. 2017 ; 21( 5): 1061-1095.[citado 2024 nov. 04 ] Available from: https://doi.org/10.3233/IDA-163075

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