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Artigo de periodico
Predicting and interpreting oxide glass properties by machine learning using large datasets (2021)
Cassar, Daniel Roberto ; Mastelini, Saulo Martiello ; Botari, Tiago ; Alcobaça, Edesio ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Zanotto, Edgar Dutra
Source: Ceramics International. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS, PROPRIEDADES DOS MATERIAIS, VIDRO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASSAR, Daniel Roberto et al. Predicting and interpreting oxide glass properties by machine learning using large datasets. Ceramics International, v. 47, n. 17, p. Se 2021, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ceramint.2021.05.105. Acesso em: 29 jun. 2024.
APA
Cassar, D. R., Mastelini, S. M., Botari, T., Alcobaça, E., Carvalho, A. C. P. de L. F. de, & Zanotto, E. D. (2021). Predicting and interpreting oxide glass properties by machine learning using large datasets. Ceramics International, 47( 17), Se 2021. doi:10.1016/j.ceramint.2021.05.105
NLM
Cassar DR, Mastelini SM, Botari T, Alcobaça E, Carvalho ACP de LF de, Zanotto ED. Predicting and interpreting oxide glass properties by machine learning using large datasets [Internet]. Ceramics International. 2021 ; 47( 17): Se 2021.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ceramint.2021.05.105
Vancouver
Cassar DR, Mastelini SM, Botari T, Alcobaça E, Carvalho ACP de LF de, Zanotto ED. Predicting and interpreting oxide glass properties by machine learning using large datasets [Internet]. Ceramics International. 2021 ; 47( 17): Se 2021.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ceramint.2021.05.105
Artigo de periodico
Explainable machine learning algorithms for predicting glass transition temperatures (2020)
Alcobaça, Edesio ; Mastelini, Saulo Martiello ; Botari, Tiago ; Pimentel, Bruno Almeida ; Cassar, Daniel Roberto ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Zanotto, Edgar Dutra
Source: Acta Materialia. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS, VIDRO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALCOBAÇA, Edesio et al. Explainable machine learning algorithms for predicting glass transition temperatures. Acta Materialia, v. 188, p. 92-100, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.actamat.2020.01.047. Acesso em: 29 jun. 2024.
APA
Alcobaça, E., Mastelini, S. M., Botari, T., Pimentel, B. A., Cassar, D. R., Carvalho, A. C. P. de L. F. de, & Zanotto, E. D. (2020). Explainable machine learning algorithms for predicting glass transition temperatures. Acta Materialia, 188, 92-100. doi:10.1016/j.actamat.2020.01.047
NLM
Alcobaça E, Mastelini SM, Botari T, Pimentel BA, Cassar DR, Carvalho ACP de LF de, Zanotto ED. Explainable machine learning algorithms for predicting glass transition temperatures [Internet]. Acta Materialia. 2020 ; 188 92-100.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.actamat.2020.01.047
Vancouver
Alcobaça E, Mastelini SM, Botari T, Pimentel BA, Cassar DR, Carvalho ACP de LF de, Zanotto ED. Explainable machine learning algorithms for predicting glass transition temperatures [Internet]. Acta Materialia. 2020 ; 188 92-100.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.actamat.2020.01.047
Trabalho de evento-anais periodico
Local interpretation methods to machine learning using the domain of the feature space (2020)
Botari, Tiago ; Izbicki, Rafael ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Communications in Computer and Information Science. Conference titles: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, COMBINATÓRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BOTARI, Tiago e IZBICKI, Rafael e CARVALHO, André Carlos Ponce de Leon Ferreira de. Local interpretation methods to machine learning using the domain of the feature space. Communications in Computer and Information Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-43823-4_21. Acesso em: 29 jun. 2024. , 2020
APA
Botari, T., Izbicki, R., & Carvalho, A. C. P. de L. F. de. (2020). Local interpretation methods to machine learning using the domain of the feature space. Communications in Computer and Information Science. Cham: Springer. doi:10.1007/978-3-030-43823-4_21
NLM
Botari T, Izbicki R, Carvalho ACP de LF de. Local interpretation methods to machine learning using the domain of the feature space [Internet]. Communications in Computer and Information Science. 2020 ; 1167 241-252.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-030-43823-4_21
Vancouver
Botari T, Izbicki R, Carvalho ACP de LF de. Local interpretation methods to machine learning using the domain of the feature space [Internet]. Communications in Computer and Information Science. 2020 ; 1167 241-252.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-030-43823-4_21

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