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Addressing the gap between current language models and key-term-based clustering (2023)
Cabral, Eric Macedo ; Rezaeipourfarsangi, Sima ; Oliveira, Maria Cristina Ferreira de ; Milios, Evangelos E ; Minghim, Rosane
Source: Proceedings. Conference titles: ACM Symposium on Document Engineering - DocEng. Unidade: ICMC
Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DE TEXTO, REDES NEURAIS, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CABRAL, Eric Macedo et al. Addressing the gap between current language models and key-term-based clustering. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3573128.3604900. Acesso em: 09 nov. 2024.
APA
Cabral, E. M., Rezaeipourfarsangi, S., Oliveira, M. C. F. de, Milios, E. E., & Minghim, R. (2023). Addressing the gap between current language models and key-term-based clustering. In Proceedings. New York: ACM. doi:10.1145/3573128.3604900
NLM
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 nov. 09 ] Available from: https://doi.org/10.1145/3573128.3604900
Vancouver
Cabral EM, Rezaeipourfarsangi S, Oliveira MCF de, Milios EE, Minghim R. Addressing the gap between current language models and key-term-based clustering [Internet]. Proceedings. 2023 ;[citado 2024 nov. 09 ] Available from: https://doi.org/10.1145/3573128.3604900
Artigo de periodico
Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space (2022)
Soares, Juliana Coatrini ; Soares, Andrey Coatrini ; Popolin Neto, Mário ; Paulovich, Fernando Vieira ; Oliveira Junior, Osvaldo Novais de ; Mattoso, Luiz Henrique Capparelli
Source: Sensors and Actuators Reports. Unidades: ICMC, IFSC
Subjects: APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA, MASTITE ANIMAL, PECUÁRIA LEITEIRA, STAPHYLOCOCCUS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOARES, Juliana Coatrini et al. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space. Sensors and Actuators Reports, v. No 2022, p. 100083-1-100083-10, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.snr.2022.100083. Acesso em: 09 nov. 2024.
APA
Soares, J. C., Soares, A. C., Popolin Neto, M., Paulovich, F. V., Oliveira Junior, O. N. de, & Mattoso, L. H. C. (2022). Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space. Sensors and Actuators Reports, No 2022, 100083-1-100083-10. doi:10.1016/j.snr.2022.100083
NLM
Soares JC, Soares AC, Popolin Neto M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space [Internet]. Sensors and Actuators Reports. 2022 ; No 2022 100083-1-100083-10.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.snr.2022.100083
Vancouver
Soares JC, Soares AC, Popolin Neto M, Paulovich FV, Oliveira Junior ON de, Mattoso LHC. Detection of Staphylococcus aureus in milk samples using impedance spectroscopy and data processing with information visualization techniques and multidimensional calibration space [Internet]. Sensors and Actuators Reports. 2022 ; No 2022 100083-1-100083-10.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.snr.2022.100083
Artigo de periodico
Neural network training fingerprint: visual analytics of the training process in classification neural networks (2022)
Ferreira, Martha Dais ; Cantareira, Gabriel Dias ; Mello, Rodrigo Fernandes de ; Paulovich, Fernando Vieira
Source: Journal of Visualization. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, VISUALIZAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERREIRA, Martha Dais et al. Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, v. 25, n. 3, p. 593-612, 2022Tradução . . Disponível em: https://doi.org/10.1007/s12650-021-00809-4. Acesso em: 09 nov. 2024.
APA
Ferreira, M. D., Cantareira, G. D., Mello, R. F. de, & Paulovich, F. V. (2022). Neural network training fingerprint: visual analytics of the training process in classification neural networks. Journal of Visualization, 25( 3), 593-612. doi:10.1007/s12650-021-00809-4
NLM
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Vancouver
Ferreira MD, Cantareira GD, Mello RF de, Paulovich FV. Neural network training fingerprint: visual analytics of the training process in classification neural networks [Internet]. Journal of Visualization. 2022 ; 25( 3): 593-612.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1007/s12650-021-00809-4
Artigo de periodico
A graph-based approach for positive and unlabeled learning (2021)
Carnevali, Julio César ; Rossi, Rafael Geraldeli ; Milios, Evangelos ; Lopes, Alneu de Andrade
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE TEXTO, RECUPERAÇÃO DA INFORMAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARNEVALI, Julio César et al. A graph-based approach for positive and unlabeled learning. Information Sciences, v. No 2021, p. 655-672, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2021.08.099. Acesso em: 09 nov. 2024.
APA
Carnevali, J. C., Rossi, R. G., Milios, E., & Lopes, A. de A. (2021). A graph-based approach for positive and unlabeled learning. Information Sciences, No 2021, 655-672. doi:10.1016/j.ins.2021.08.099
NLM
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099
Vancouver
Carnevali JC, Rossi RG, Milios E, Lopes A de A. A graph-based approach for positive and unlabeled learning [Internet]. Information Sciences. 2021 ; No 2021 655-672.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.ins.2021.08.099
Artigo de periodico
Explainable matrix: visualization for global and local interpretability of random forest classification ensembles (2021)
Popolin Neto, Mário ; Paulovich, Fernando Vieira
Source: IEEE Transactions on Visualization and Computer Graphics. Unidade: ICMC
Subjects: VISUALIZAÇÃO, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário e PAULOVICH, Fernando Vieira. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, v. 27, n. 2, p. 1427-1437, 2021Tradução . . Disponível em: https://doi.org/10.1109/TVCG.2020.3030354. Acesso em: 09 nov. 2024.
APA
Popolin Neto, M., & Paulovich, F. V. (2021). Explainable matrix: visualization for global and local interpretability of random forest classification ensembles. IEEE Transactions on Visualization and Computer Graphics, 27( 2), 1427-1437. doi:10.1109/TVCG.2020.3030354
NLM
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Vancouver
Popolin Neto M, Paulovich FV. Explainable matrix: visualization for global and local interpretability of random forest classification ensembles [Internet]. IEEE Transactions on Visualization and Computer Graphics. 2021 ; 27( 2): 1427-1437.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1109/TVCG.2020.3030354
Artigo de periodico
Machine learning used to create a multidimensional calibration space for sensing and biosensing data (2021)
Popolin Neto, Mário ; Soares, Andrey Coatrini ; Oliveira Junior, Osvaldo Novais de ; Paulovich, Fernando Vieira
Source: Bulletin of the Chemical Society of Japan. Unidades: IFSC, ICMC
Subjects: BIOTECNOLOGIA, APRENDIZADO COMPUTACIONAL, SENSOR, FILMES FINOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POPOLIN NETO, Mário et al. Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, v. 94, n. 5, p. 1553-1562, 2021Tradução . . Disponível em: https://doi.org/10.1246/bcsj.20200359. Acesso em: 09 nov. 2024.
APA
Popolin Neto, M., Soares, A. C., Oliveira Junior, O. N. de, & Paulovich, F. V. (2021). Machine learning used to create a multidimensional calibration space for sensing and biosensing data. Bulletin of the Chemical Society of Japan, 94( 5), 1553-1562. doi:10.1246/bcsj.20200359
NLM
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1246/bcsj.20200359
Vancouver
Popolin Neto M, Soares AC, Oliveira Junior ON de, Paulovich FV. Machine learning used to create a multidimensional calibration space for sensing and biosensing data [Internet]. Bulletin of the Chemical Society of Japan. 2021 ; 94( 5): 1553-1562.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1246/bcsj.20200359

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