Local interpretation methods to machine learning using the domain of the feature space (2020)
- Authors:
- USP affiliated authors: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; BOTARI, TIAGO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-43823-4_21
- Subjects: APRENDIZADO COMPUTACIONAL; COMBINATÓRIA
- Keywords: Interpretability; Local estimation
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Communications in Computer and Information Science
- ISSN: 1865-0929
- Volume/Número/Paginação/Ano: v. 1167, p. 241-252, 2020
- Conference titles: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
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: 03 dez. 2025. , 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 2025 dez. 03 ] 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 2025 dez. 03 ] Available from: https://doi.org/10.1007/978-3-030-43823-4_21 - Hydrogen evolution at the in-situ MoO3/MoS2 heterojunctions created by non-thermal O2 plasma treatment
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Informações sobre o DOI: 10.1007/978-3-030-43823-4_21 (Fonte: oaDOI API)
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