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Parte de monografia/livro
Automatic insect classification using Bayesian classifier and audio processing features (2015)
Silva, D. F; Chen, Y; Why, A; Keogh, E. J; Batista, Gustavo Enrique de Almeida Prado Alves
Source: The plant protection data toolbox. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, CONTROLE DE INSETOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, D. F et al. Automatic insect classification using Bayesian classifier and audio processing features. The plant protection data toolbox. Tradução . Caxton: New Zealand Plant Protection Society, 2015. . . Acesso em: 01 jun. 2024.
APA
Silva, D. F., Chen, Y., Why, A., Keogh, E. J., & Batista, G. E. de A. P. A. (2015). Automatic insect classification using Bayesian classifier and audio processing features. In The plant protection data toolbox. Caxton: New Zealand Plant Protection Society.
NLM
Silva DF, Chen Y, Why A, Keogh EJ, Batista GE de APA. Automatic insect classification using Bayesian classifier and audio processing features. In: The plant protection data toolbox. Caxton: New Zealand Plant Protection Society; 2015. [citado 2024 jun. 01 ]
Vancouver
Silva DF, Chen Y, Why A, Keogh EJ, Batista GE de APA. Automatic insect classification using Bayesian classifier and audio processing features. In: The plant protection data toolbox. Caxton: New Zealand Plant Protection Society; 2015. [citado 2024 jun. 01 ]
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
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: 01 jun. 2024.
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 2024 jun. 01 ] 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 2024 jun. 01 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
Trabalho de evento
Applying machine learning and audio analysis techniques to insect recognition in intelligent traps (2013)
Silva, Diego F; Souza, Vinícius M. A. de; Batista, Gustavo Enrique de Almeida Prado Alves; Keogh, Eamonn; Ellis, Daniel P. W
Source: Proceedings. Conference titles: International Conference on Machine Learning and Applications - ICMLA. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Diego F et al. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. 2013, Anais.. Los Alamitos: CPS/IEEE, 2013. Disponível em: https://doi.org/10.1109/ICMLA.2013.24. Acesso em: 01 jun. 2024.
APA
Silva, D. F., Souza, V. M. A. de, Batista, G. E. de A. P. A., Keogh, E., & Ellis, D. P. W. (2013). Applying machine learning and audio analysis techniques to insect recognition in intelligent traps. In Proceedings. Los Alamitos: CPS/IEEE. doi:10.1109/ICMLA.2013.24
NLM
Silva DF, Souza VMA de, Batista GE de APA, Keogh E, Ellis DPW. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1109/ICMLA.2013.24
Vancouver
Silva DF, Souza VMA de, Batista GE de APA, Keogh E, Ellis DPW. Applying machine learning and audio analysis techniques to insect recognition in intelligent traps [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1109/ICMLA.2013.24
Artigo de periodico
Addressing big data time series: mining trillions of time series subsequences under dynamic time warping (2013)
Rakthanmanon, Thanawin; Campana, Bilson; Mueen, Abdullah; Batista, Gustavo Enrique de Almeida Prado Alves; Westover, Brandon; Zhu, Qiang; Zakaria, Jesin; Keogh, Eamonn
Source: ACM Transactions on Knowledge Discovery from Data - TKDD. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAKTHANMANON, Thanawin et al. Addressing big data time series: mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data - TKDD, v. 7, n. 3, p. 10:1-10:31, 2013Tradução . . Disponível em: https://doi.org/10.1145/2500489. Acesso em: 01 jun. 2024.
APA
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G. E. de A. P. A., Westover, B., Zhu, Q., et al. (2013). Addressing big data time series: mining trillions of time series subsequences under dynamic time warping. ACM Transactions on Knowledge Discovery from Data - TKDD, 7( 3), 10:1-10:31. doi:10.1145/2500489
NLM
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Zhu Q, Zakaria J, Keogh E. Addressing big data time series: mining trillions of time series subsequences under dynamic time warping [Internet]. ACM Transactions on Knowledge Discovery from Data - TKDD. 2013 ; 7( 3): 10:1-10:31.[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2500489
Vancouver
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Zhu Q, Zakaria J, Keogh E. Addressing big data time series: mining trillions of time series subsequences under dynamic time warping [Internet]. ACM Transactions on Knowledge Discovery from Data - TKDD. 2013 ; 7( 3): 10:1-10:31.[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2500489
Trabalho de evento
DTW-D: time series semi-supervised learning from a single example (2013)
Chen, Yanping; Hu, Bing; Keogh, Eamonn; Batista, Gustavo Enrique de Almeida Prado Alves
Source: Proceedings. Conference titles: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CHEN, Yanping et al. DTW-D: time series semi-supervised learning from a single example. 2013, Anais.. New York: ACM, 2013. Disponível em: https://doi.org/10.1145/2487575.2487633. Acesso em: 01 jun. 2024.
APA
Chen, Y., Hu, B., Keogh, E., & Batista, G. E. de A. P. A. (2013). DTW-D: time series semi-supervised learning from a single example. In Proceedings. New York: ACM. doi:10.1145/2487575.2487633
NLM
Chen Y, Hu B, Keogh E, Batista GE de APA. DTW-D: time series semi-supervised learning from a single example [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2487575.2487633
Vancouver
Chen Y, Hu B, Keogh E, Batista GE de APA. DTW-D: time series semi-supervised learning from a single example [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2487575.2487633
Trabalho de evento
Data mining a trillion time series subsequences under dynamic time warping (2013)
Rakthanmanon, Thanawin; Campana, Bilson; Mueen, Abdullah; Batista, Gustavo Enrique de Almeida Prado Alves; Westover, Brandon; Qiang, Zhu; Zakaria, Jesin; Keogh, Eamonn
Source: Proceedings. Conference titles: International Joint Conference on Artificial Intelligence - IJCAI. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAKTHANMANON, Thanawin et al. Data mining a trillion time series subsequences under dynamic time warping. 2013, Anais.. Menlo Park: AAAI Press, 2013. Disponível em: http://ijcai.org/papers13/Papers/IJCAI13-454.pdf. Acesso em: 01 jun. 2024.
APA
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G. E. de A. P. A., Westover, B., Qiang, Z., et al. (2013). Data mining a trillion time series subsequences under dynamic time warping. In Proceedings. Menlo Park: AAAI Press. Recuperado de http://ijcai.org/papers13/Papers/IJCAI13-454.pdf
NLM
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Qiang Z, Zakaria J, Keogh E. Data mining a trillion time series subsequences under dynamic time warping [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: http://ijcai.org/papers13/Papers/IJCAI13-454.pdf
Vancouver
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Qiang Z, Zakaria J, Keogh E. Data mining a trillion time series subsequences under dynamic time warping [Internet]. Proceedings. 2013 ;[citado 2024 jun. 01 ] Available from: http://ijcai.org/papers13/Papers/IJCAI13-454.pdf
Trabalho de evento
A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets (2012)
Qiang, Zhu; Batista, Gustavo Enrique de Almeida Prado Alves; Rakthanmanon, Thanawin; Keogh, Eamonn
Source: Proceedings. Conference titles: SIAM International Conference on Data Mining. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
QIANG, Zhu et al. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. 2012, Anais.. Philadelphia: SIAM, 2012. Disponível em: http://siam.omnibooksonline.com/2012datamining/. Acesso em: 01 jun. 2024.
APA
Qiang, Z., Batista, G. E. de A. P. A., Rakthanmanon, T., & Keogh, E. (2012). A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. In Proceedings. Philadelphia: SIAM. Recuperado de http://siam.omnibooksonline.com/2012datamining/
NLM
Qiang Z, Batista GE de APA, Rakthanmanon T, Keogh E. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets [Internet]. Proceedings. 2012 ;[citado 2024 jun. 01 ] Available from: http://siam.omnibooksonline.com/2012datamining/
Vancouver
Qiang Z, Batista GE de APA, Rakthanmanon T, Keogh E. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets [Internet]. Proceedings. 2012 ;[citado 2024 jun. 01 ] Available from: http://siam.omnibooksonline.com/2012datamining/
Trabalho de evento
Searching and mining trillions of time series subsequences under dynamic time warping (2012)
Rakthanmanon, Thanawin; Campana, Bilson; Mueen, Abdullah; Batista, Gustavo Enrique de Almeida Prado Alves; Westover, Brandon; Qiang, Zhu; Zakaria, Jesin; Keogh, Eamonn
Source: Proceedings. Conference titles: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAKTHANMANON, Thanawin et al. Searching and mining trillions of time series subsequences under dynamic time warping. 2012, Anais.. New York: ACM, 2012. Disponível em: https://doi.org/10.1145/2339530.2339576. Acesso em: 01 jun. 2024.
APA
Rakthanmanon, T., Campana, B., Mueen, A., Batista, G. E. de A. P. A., Westover, B., Qiang, Z., et al. (2012). Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings. New York: ACM. doi:10.1145/2339530.2339576
NLM
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Qiang Z, Zakaria J, Keogh E. Searching and mining trillions of time series subsequences under dynamic time warping [Internet]. Proceedings. 2012 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2339530.2339576
Vancouver
Rakthanmanon T, Campana B, Mueen A, Batista GE de APA, Westover B, Qiang Z, Zakaria J, Keogh E. Searching and mining trillions of time series subsequences under dynamic time warping [Internet]. Proceedings. 2012 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1145/2339530.2339576
Trabalho de evento
Towards automatic classification on flying insects using inexpensive sensors (2011)
Batista, Gustavo Enrique de Almeida Prado Alves; Hao, Yuan; Keogh, Eamonn; Mafra-Neto, Agenor
Source: Proceedings. Conference titles: International Conference on Machine Learning and Applications - ICMLA. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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. Towards automatic classification on flying insects using inexpensive sensors. 2011, Anais.. Los Alamitos: IEEE Computer Society, 2011. Disponível em: https://doi.org/10.1109/ICMLA.2011.145. Acesso em: 01 jun. 2024.
APA
Batista, G. E. de A. P. A., Hao, Y., Keogh, E., & Mafra-Neto, A. (2011). Towards automatic classification on flying insects using inexpensive sensors. In Proceedings. Los Alamitos: IEEE Computer Society. doi:10.1109/ICMLA.2011.145
NLM
Batista GE de APA, Hao Y, Keogh E, Mafra-Neto A. Towards automatic classification on flying insects using inexpensive sensors [Internet]. Proceedings. 2011 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1109/ICMLA.2011.145
Vancouver
Batista GE de APA, Hao Y, Keogh E, Mafra-Neto A. Towards automatic classification on flying insects using inexpensive sensors [Internet]. Proceedings. 2011 ;[citado 2024 jun. 01 ] Available from: https://doi.org/10.1109/ICMLA.2011.145

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