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Artigo de periodico
Internal evaluation of unsupervised outlier detection (2020)
Marques, Henrique Oliveira; Campello, Ricardo José Gabrielli Barreto ; Sander, Jörg ; Zimek, Arthur
Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARQUES, Henrique Oliveira et al. Internal evaluation of unsupervised outlier detection. ACM Transactions on Knowledge Discovery from Data, v. 14, n. 4, p. 47:1-47:42, 2020Tradução . . Disponível em: https://doi.org/10.1145/3394053. Acesso em: 02 jun. 2024.
APA
Marques, H. O., Campello, R. J. G. B., Sander, J., & Zimek, A. (2020). Internal evaluation of unsupervised outlier detection. ACM Transactions on Knowledge Discovery from Data, 14( 4), 47:1-47:42. doi:10.1145/3394053
NLM
Marques HO, Campello RJGB, Sander J, Zimek A. Internal evaluation of unsupervised outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2020 ; 14( 4): 47:1-47:42.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3394053
Vancouver
Marques HO, Campello RJGB, Sander J, Zimek A. Internal evaluation of unsupervised outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2020 ; 14( 4): 47:1-47:42.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3394053
Artigo de periodico
ATR-Vis: visual and interactive information retrieval for parliamentary discussions in Twitter (2018)
Makki, Raheleh; Carvalho, Eder; Soto, Axel J; Brooks, Stephen; Oliveira, Maria Cristina Ferreira de ; Milios, Evangelos; Minghim, Rosane
Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Assuntos: COMPUTAÇÃO GRÁFICA, RECUPERAÇÃO DA INFORMAÇÃO, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAKKI, Raheleh et al. ATR-Vis: visual and interactive information retrieval for parliamentary discussions in Twitter. ACM Transactions on Knowledge Discovery from Data, v. 12, n. 1, p. 3:1-3:33, 2018Tradução . . Disponível em: https://doi.org/10.1145/3047010. Acesso em: 02 jun. 2024.
APA
Makki, R., Carvalho, E., Soto, A. J., Brooks, S., Oliveira, M. C. F. de, Milios, E., & Minghim, R. (2018). ATR-Vis: visual and interactive information retrieval for parliamentary discussions in Twitter. ACM Transactions on Knowledge Discovery from Data, 12( 1), 3:1-3:33. doi:10.1145/3047010
NLM
Makki R, Carvalho E, Soto AJ, Brooks S, Oliveira MCF de, Milios E, Minghim R. ATR-Vis: visual and interactive information retrieval for parliamentary discussions in Twitter [Internet]. ACM Transactions on Knowledge Discovery from Data. 2018 ; 12( 1): 3:1-3:33.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3047010
Vancouver
Makki R, Carvalho E, Soto AJ, Brooks S, Oliveira MCF de, Milios E, Minghim R. ATR-Vis: visual and interactive information retrieval for parliamentary discussions in Twitter [Internet]. ACM Transactions on Knowledge Discovery from Data. 2018 ; 12( 1): 3:1-3:33.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3047010
Artigo de periodico
Modeling temporal activity to detect anomalous behavior in social media (2017)
Costa, Alceu Ferraz; Yamaguchi, Yuto; Traina, Agma Juci Machado; Traina Junior, Caetano; Faloutsos, Christos
Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Assuntos: BANCO DE DADOS, MINERAÇÃO DE DADOS, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COSTA, Alceu Ferraz et al. Modeling temporal activity to detect anomalous behavior in social media. ACM Transactions on Knowledge Discovery from Data, v. 11, n. 4. p. 49:1-49:23, 2017Tradução . . Disponível em: https://doi.org/10.1145/3064884. Acesso em: 02 jun. 2024.
APA
Costa, A. F., Yamaguchi, Y., Traina, A. J. M., Traina Junior, C., & Faloutsos, C. (2017). Modeling temporal activity to detect anomalous behavior in social media. ACM Transactions on Knowledge Discovery from Data, 11( 4. p. 49:1-49:23). doi:10.1145/3064884
NLM
Costa AF, Yamaguchi Y, Traina AJM, Traina Junior C, Faloutsos C. Modeling temporal activity to detect anomalous behavior in social media [Internet]. ACM Transactions on Knowledge Discovery from Data. 2017 ; 11( 4. p. 49:1-49:23):[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3064884
Vancouver
Costa AF, Yamaguchi Y, Traina AJM, Traina Junior C, Faloutsos C. Modeling temporal activity to detect anomalous behavior in social media [Internet]. ACM Transactions on Knowledge Discovery from Data. 2017 ; 11( 4. p. 49:1-49:23):[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/3064884
Artigo de periodico
Hierarchical density estimates for data clustering, visualization, and outlier detection (2015)
Campello, Ricardo José Gabrielli Barreto; Moulavi, Davoud; Zimek, Arthur; Sander, Jörg
Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Assuntos: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS, RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAMPELLO, Ricardo José Gabrielli Barreto et al. Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, v. 10, n. 1, p. 5:1-5:51, 2015Tradução . . Disponível em: https://doi.org/10.1145/2733381. Acesso em: 02 jun. 2024.
APA
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 10( 1), 5:1-5:51. doi:10.1145/2733381
NLM
Campello RJGB, Moulavi D, Zimek A, Sander J. Hierarchical density estimates for data clustering, visualization, and outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2015 ; 10( 1): 5:1-5:51.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/2733381
Vancouver
Campello RJGB, Moulavi D, Zimek A, Sander J. Hierarchical density estimates for data clustering, visualization, and outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2015 ; 10( 1): 5:1-5:51.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/2733381
Artigo de periodico
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning (2014)
Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Acharyya, Sreangsu
Fonte: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Assuntos: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ACHARYA, Ayan et al. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, v. 9, n. 1, p. 1:1-1:35, 2014Tradução . . Disponível em: https://doi.org/10.1145/2601435. Acesso em: 02 jun. 2024.
APA
Acharya, A., Hruschka, E. R., Ghosh, J., & Acharyya, S. (2014). An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, 9( 1), 1:1-1:35. doi:10.1145/2601435
NLM
Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/2601435
Vancouver
Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 jun. 02 ] Available from: https://doi.org/10.1145/2601435

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