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Trabalho de evento
Accurately quantifying a billion instances per second (2020)
Hassan, Waqar; Maletzke, André Gustavo ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: IEEE International Conference on Data Science and Advanced Analytics - DSAA. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS
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ABNT
HASSAN, Waqar e MALETZKE, André Gustavo e BATISTA, Gustavo Enrique de Almeida Prado Alves. Accurately quantifying a billion instances per second. 2020, Anais.. Los Alamitos: IEEE, 2020. Disponível em: https://doi.org/10.1109/DSAA49011.2020.00012. Acesso em: 04 set. 2024.
APA
Hassan, W., Maletzke, A. G., & Batista, G. E. de A. P. A. (2020). Accurately quantifying a billion instances per second. In Proceedings. Los Alamitos: IEEE. doi:10.1109/DSAA49011.2020.00012
NLM
Hassan W, Maletzke AG, Batista GE de APA. Accurately quantifying a billion instances per second [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/DSAA49011.2020.00012
Vancouver
Hassan W, Maletzke AG, Batista GE de APA. Accurately quantifying a billion instances per second [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/DSAA49011.2020.00012
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
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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: 04 set. 2024.
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 2024 set. 04 ] 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 2024 set. 04 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
Trabalho de evento
Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline (2020)
Jacintho, Lucas Henrique Mantovani; Silva, Tiago Pinho da ; Parmezan, Antonio Rafael Sabino ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ANÁLISE DE SÉRIES TEMPORAIS, ELEIÇÕES DIRETAS
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ABNT
JACINTHO, Lucas Henrique Mantovani et al. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11979. Acesso em: 04 set. 2024.
APA
Jacintho, L. H. M., Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2020). Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11979
NLM
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.5753/kdmile.2020.11979
Vancouver
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.5753/kdmile.2020.11979
Trabalho de evento
The importance of the test set size in quantification assessment (2020)
Maletzke, André Gustavo ; Hassan, Waqar; Reis, Denis Moreira dos ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL
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ABNT
MALETZKE, André Gustavo et al. The importance of the test set size in quantification assessment. 2020, Anais.. Vienna: IJCAI, 2020. Disponível em: https://doi.org/10.24963/ijcai.2020/366. Acesso em: 04 set. 2024.
APA
Maletzke, A. G., Hassan, W., Reis, D. M. dos, & Batista, G. E. de A. P. A. (2020). The importance of the test set size in quantification assessment. In Proceedings. Vienna: IJCAI. doi:10.24963/ijcai.2020/366
NLM
Maletzke AG, Hassan W, Reis DM dos, Batista GE de APA. The importance of the test set size in quantification assessment [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.24963/ijcai.2020/366
Vancouver
Maletzke AG, Hassan W, Reis DM dos, Batista GE de APA. The importance of the test set size in quantification assessment [Internet]. Proceedings. 2020 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.24963/ijcai.2020/366
Trabalho de evento-anais periodico
One-class quantification (2019)
Reis, Denis dos; Maletzke, André Gustavo; Cherman, Everton Alvares; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Lecture Notes in Artificial Intelligence. Conference titles: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES
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ABNT
REIS, Denis dos et al. One-class quantification. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-10925-7_17. Acesso em: 04 set. 2024. , 2019
APA
Reis, D. dos, Maletzke, A. G., Cherman, E. A., & Batista, G. E. de A. P. A. (2019). One-class quantification. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-10925-7_17
NLM
Reis D dos, Maletzke AG, Cherman EA, Batista GE de APA. One-class quantification [Internet]. Lecture Notes in Artificial Intelligence. 2019 ; 11051 273-289.[citado 2024 set. 04 ] Available from: https://doi.org/10.1007/978-3-030-10925-7_17
Vancouver
Reis D dos, Maletzke AG, Cherman EA, Batista GE de APA. One-class quantification [Internet]. Lecture Notes in Artificial Intelligence. 2019 ; 11051 273-289.[citado 2024 set. 04 ] Available from: https://doi.org/10.1007/978-3-030-10925-7_17
Trabalho de evento-anais periodico
Towards hierarchical classification of data streams (2019)
Parmezan, Antonio Rafael Sabino; Souza, Vinícius M. A; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Lecture Notes in Computer Science. Conference titles: Iberoamerican Congress on Pattern Recognition - CIARP : Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL
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ABNT
PARMEZAN, Antonio Rafael Sabino e SOUZA, Vinícius M. A e BATISTA, Gustavo Enrique de Almeida Prado Alves. Towards hierarchical classification of data streams. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-13469-3_37. Acesso em: 04 set. 2024. , 2019
APA
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. de A. P. A. (2019). Towards hierarchical classification of data streams. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-030-13469-3_37
NLM
Parmezan ARS, Souza VMA, Batista GE de APA. Towards hierarchical classification of data streams [Internet]. Lecture Notes in Computer Science. 2019 ; 11401 314-322.[citado 2024 set. 04 ] Available from: https://doi.org/10.1007/978-3-030-13469-3_37
Vancouver
Parmezan ARS, Souza VMA, Batista GE de APA. Towards hierarchical classification of data streams [Internet]. Lecture Notes in Computer Science. 2019 ; 11401 314-322.[citado 2024 set. 04 ] Available from: https://doi.org/10.1007/978-3-030-13469-3_37
Trabalho de evento
EmbML tool: supporting the use of supervised learning algorithms in low-cost embedded systems (2019)
Silva, Lucas Tsutsui da ; Souza, Vinícius Mourão Alves de; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: IEEE International Conference on Tools with Artificial Intelligence - ICTAI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, SISTEMAS EMBUTIDOS, RECONHECIMENTO DE PADRÕES
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ABNT
SILVA, Lucas Tsutsui da e SOUZA, Vinícius Mourão Alves de e BATISTA, Gustavo Enrique de Almeida Prado Alves. EmbML tool: supporting the use of supervised learning algorithms in low-cost embedded systems. 2019, Anais.. Los Alamitos: IEEE, 2019. Disponível em: https://doi.org/10.1109/ICTAI.2019.00238. Acesso em: 04 set. 2024.
APA
Silva, L. T. da, Souza, V. M. A. de, & Batista, G. E. de A. P. A. (2019). EmbML tool: supporting the use of supervised learning algorithms in low-cost embedded systems. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICTAI.2019.00238
NLM
Silva LT da, Souza VMA de, Batista GE de APA. EmbML tool: supporting the use of supervised learning algorithms in low-cost embedded systems [Internet]. Proceedings. 2019 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/ICTAI.2019.00238
Vancouver
Silva LT da, Souza VMA de, Batista GE de APA. EmbML tool: supporting the use of supervised learning algorithms in low-cost embedded systems [Internet]. Proceedings. 2019 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/ICTAI.2019.00238
Artigo de periodico
Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model (2019)
Parmezan, Antonio Rafael Sabino; Souza, Vinícius M. A; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS
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ABNT
PARMEZAN, Antonio Rafael Sabino e SOUZA, Vinícius M. A e BATISTA, Gustavo Enrique de Almeida Prado Alves. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, v. 484, p. 302-337, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2019.01.076. Acesso em: 04 set. 2024.
APA
Parmezan, A. R. S., Souza, V. M. A., & Batista, G. E. de A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, 484, 302-337. doi:10.1016/j.ins.2019.01.076
NLM
Parmezan ARS, Souza VMA, Batista GE de APA. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model [Internet]. Information Sciences. 2019 ; 484 302-337.[citado 2024 set. 04 ] Available from: https://doi.org/10.1016/j.ins.2019.01.076
Vancouver
Parmezan ARS, Souza VMA, Batista GE de APA. Evaluation of statistical and machine learning models for time series prediction: identifying the state-of-the-art and the best conditions for the use of each model [Internet]. Information Sciences. 2019 ; 484 302-337.[citado 2024 set. 04 ] Available from: https://doi.org/10.1016/j.ins.2019.01.076
Trabalho de evento
DyS: a framework for mixture models in quantification (2019)
Maletzke, André Gustavo; Reis, Denis dos; Cherman, Everton Alvares; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MÉTODOS ESTATÍSTICOS PARA APRENDIZAGEM
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ABNT
MALETZKE, André Gustavo et al. DyS: a framework for mixture models in quantification. 2019, Anais.. Palo Alto: AAAI Press, 2019. Disponível em: https://doi.org/10.1609/aaai.v33i01.33014552. Acesso em: 04 set. 2024.
APA
Maletzke, A. G., Reis, D. dos, Cherman, E. A., & Batista, G. E. de A. P. A. (2019). DyS: a framework for mixture models in quantification. In Proceedings. Palo Alto: AAAI Press. doi:10.1609/aaai.v33i01.33014552
NLM
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. DyS: a framework for mixture models in quantification [Internet]. Proceedings. 2019 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1609/aaai.v33i01.33014552
Vancouver
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. DyS: a framework for mixture models in quantification [Internet]. Proceedings. 2019 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1609/aaai.v33i01.33014552
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
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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 set. 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 set. 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 set. 04 ] Available from: https://doi.org/10.1109/TMM.2018.2849563
Trabalho de evento
Unsupervised context switch for classification tasks on data streams with recurrent concepts (2018)
Reis, Denis M. dos; Maletzke, André G; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: Symposium on Applied Computing - SAC. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MATEMÁTICA DA COMPUTAÇÃO, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
REIS, Denis M. dos e MALETZKE, André G e BATISTA, Gustavo Enrique de Almeida Prado Alves. Unsupervised context switch for classification tasks on data streams with recurrent concepts. 2018, Anais.. New York: ACM, 2018. Disponível em: https://doi.org/10.1145/3167132.3167189. Acesso em: 04 set. 2024.
APA
Reis, D. M. dos, Maletzke, A. G., & Batista, G. E. de A. P. A. (2018). Unsupervised context switch for classification tasks on data streams with recurrent concepts. In Proceedings. New York: ACM. doi:10.1145/3167132.3167189
NLM
Reis DM dos, Maletzke AG, Batista GE de APA. Unsupervised context switch for classification tasks on data streams with recurrent concepts [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1145/3167132.3167189
Vancouver
Reis DM dos, Maletzke AG, Batista GE de APA. Unsupervised context switch for classification tasks on data streams with recurrent concepts [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1145/3167132.3167189
Trabalho de evento
Evaluating stream classifiers with delayed labels information (2018)
Souza, Vinícius Mourão Alves de; Silva, Tiago Pinho da; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. 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 Mourão Alves de e SILVA, Tiago Pinho da e BATISTA, Gustavo Enrique de Almeida Prado Alves. Evaluating stream classifiers with delayed labels information. 2018, Anais.. Piscataway: IEEE, 2018. Disponível em: https://doi.org/10.1109/BRACIS.2018.00077. Acesso em: 04 set. 2024.
APA
Souza, V. M. A. de, Silva, T. P. da, & Batista, G. E. de A. P. A. (2018). Evaluating stream classifiers with delayed labels information. In Proceedings. Piscataway: IEEE. doi:10.1109/BRACIS.2018.00077
NLM
Souza VMA de, Silva TP da, Batista GE de APA. Evaluating stream classifiers with delayed labels information [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/BRACIS.2018.00077
Vancouver
Souza VMA de, Silva TP da, Batista GE de APA. Evaluating stream classifiers with delayed labels information [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/BRACIS.2018.00077
Trabalho de evento-anais periodico
On the need of class ratio insensitive drift tests for data streams (2018)
Maletzke, André Gustavo; Reis, Denis dos; Cherman, Everton Alvares; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings of Machine Learning Research : PMLR. Conference titles: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database - ECML PKDD. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALETZKE, André Gustavo et al. On the need of class ratio insensitive drift tests for data streams. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: http://proceedings.mlr.press/v94/maletzke18a.html. Acesso em: 04 set. 2024. , 2018
APA
Maletzke, A. G., Reis, D. dos, Cherman, E. A., & Batista, G. E. de A. P. A. (2018). On the need of class ratio insensitive drift tests for data streams. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de http://proceedings.mlr.press/v94/maletzke18a.html
NLM
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. On the need of class ratio insensitive drift tests for data streams [Internet]. Proceedings of Machine Learning Research : PMLR. 2018 ; 94 110-124.[citado 2024 set. 04 ] Available from: http://proceedings.mlr.press/v94/maletzke18a.html
Vancouver
Maletzke AG, Reis D dos, Cherman EA, Batista GE de APA. On the need of class ratio insensitive drift tests for data streams [Internet]. Proceedings of Machine Learning Research : PMLR. 2018 ; 94 110-124.[citado 2024 set. 04 ] Available from: http://proceedings.mlr.press/v94/maletzke18a.html
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 set. 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 set. 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 set. 04 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
Trabalho de evento-resumo
Automatic insect recognition with optical sensors with variability of temperature and humidity (2018)
Batista, Gustavo Enrique de Almeida Prado Alves ; Maletzke, André Gustavo ; Milaré, Cláudia Regina; Nadai, Barbara Lepretti de ; Saroli, Jesse ; Singh, Shailendra; Corbi, Juliano José ; Keogh, Eamonn; Mafra-Neto, Agenor
Source: Abstracts. Conference titles: AMCA Annual Meeting. Unidades: ICMC, EESC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, CONTROLE DE VETORES, ARMADILHAS, AEDES
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. Automatic insect recognition with optical sensors with variability of temperature and humidity. 2018, Anais.. Sacramento: American Mosquito Control Association, 2018. Disponível em: https://ashq.eventsair.com/QuickEventWebsitePortal/amca-84th-annual-meeting/amca84annmtgevs/ExtraContent/ContentSubPage?page=2&subPage=2. Acesso em: 04 set. 2024.
APA
Batista, G. E. de A. P. A., Maletzke, A. G., Milaré, C. R., Nadai, B. L. de, Saroli, J., Singh, S., et al. (2018). Automatic insect recognition with optical sensors with variability of temperature and humidity. In Abstracts. Sacramento: American Mosquito Control Association. Recuperado de https://ashq.eventsair.com/QuickEventWebsitePortal/amca-84th-annual-meeting/amca84annmtgevs/ExtraContent/ContentSubPage?page=2&subPage=2
NLM
Batista GE de APA, Maletzke AG, Milaré CR, Nadai BL de, Saroli J, Singh S, Corbi JJ, Keogh E, Mafra-Neto A. Automatic insect recognition with optical sensors with variability of temperature and humidity [Internet]. Abstracts. 2018 ;[citado 2024 set. 04 ] Available from: https://ashq.eventsair.com/QuickEventWebsitePortal/amca-84th-annual-meeting/amca84annmtgevs/ExtraContent/ContentSubPage?page=2&subPage=2
Vancouver
Batista GE de APA, Maletzke AG, Milaré CR, Nadai BL de, Saroli J, Singh S, Corbi JJ, Keogh E, Mafra-Neto A. Automatic insect recognition with optical sensors with variability of temperature and humidity [Internet]. Abstracts. 2018 ;[citado 2024 set. 04 ] Available from: https://ashq.eventsair.com/QuickEventWebsitePortal/amca-84th-annual-meeting/amca84annmtgevs/ExtraContent/ContentSubPage?page=2&subPage=2
Trabalho de evento
Classifying and counting with recurrent contexts (2018)
Reis, Denis; Maletzke, André Gustavo; Silva, Diego Furtado ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
REIS, Denis et al. Classifying and counting with recurrent contexts. 2018, Anais.. New York: ACM, 2018. Disponível em: https://doi.org/10.1145/3219819.3220059. Acesso em: 04 set. 2024.
APA
Reis, D., Maletzke, A. G., Silva, D. F., & Batista, G. E. de A. P. A. (2018). Classifying and counting with recurrent contexts. In Proceedings. New York: ACM. doi:10.1145/3219819.3220059
NLM
Reis D, Maletzke AG, Silva DF, Batista GE de APA. Classifying and counting with recurrent contexts [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1145/3219819.3220059
Vancouver
Reis D, Maletzke AG, Silva DF, Batista GE de APA. Classifying and counting with recurrent contexts [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1145/3219819.3220059
Trabalho de evento
Large-scale similarity-based time series mining (2018)
Silva, Diego Furtado ; Batista, Gustavo Enrique de Almeida Prado Alves (Orientador) ; Keogh, Eamonn (coorient)
Source: Anais. Conference titles: Congresso da Sociedade Brasileira de Computação - CSBC. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Diego Furtado. Large-scale similarity-based time series mining. 2018, Anais.. Porto Alegre: SBC, 2018. Disponível em: https://sol.sbc.org.br/index.php/ctd/article/view/3656/3608. Acesso em: 04 set. 2024.
APA
Silva, D. F. (2018). Large-scale similarity-based time series mining. In Anais. Porto Alegre: SBC. Recuperado de https://sol.sbc.org.br/index.php/ctd/article/view/3656/3608
NLM
Silva DF. Large-scale similarity-based time series mining [Internet]. Anais. 2018 ;[citado 2024 set. 04 ] Available from: https://sol.sbc.org.br/index.php/ctd/article/view/3656/3608
Vancouver
Silva DF. Large-scale similarity-based time series mining [Internet]. Anais. 2018 ;[citado 2024 set. 04 ] Available from: https://sol.sbc.org.br/index.php/ctd/article/view/3656/3608
Trabalho de evento
Elastic time series motifs and discords (2018)
Silva, Diego Furtado ; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: IEEE International Conference on Machine Learning and Applications - ICMLA. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Diego Furtado e BATISTA, Gustavo Enrique de Almeida Prado Alves. Elastic time series motifs and discords. 2018, Anais.. Los Alamitos: IEEE, 2018. Disponível em: https://doi.org/10.1109/ICMLA.2018.00042. Acesso em: 04 set. 2024.
APA
Silva, D. F., & Batista, G. E. de A. P. A. (2018). Elastic time series motifs and discords. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICMLA.2018.00042
NLM
Silva DF, Batista GE de APA. Elastic time series motifs and discords [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/ICMLA.2018.00042
Vancouver
Silva DF, Batista GE de APA. Elastic time series motifs and discords [Internet]. Proceedings. 2018 ;[citado 2024 set. 04 ] Available from: https://doi.org/10.1109/ICMLA.2018.00042
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 set. 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 set. 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 set. 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 set. 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 set. 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 set. 04 ] Available from: https://doi.org/10.3233/IDA-163075

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