Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market (2022)
- Authors:
- USP affiliated authors: COSTA, ANNA HELENA REALI - EP ; HERNANDEZ, EMÍLIO DEL MORAL - EP
- Unidade: EP
- DOI: 10.1016/j.eswa.2022.117259
- Assunto: APRENDIZADO COMPUTACIONAL
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Expert Systems with Applications
- ISSN: 0957-4174
- Volume/Número/Paginação/Ano: v. 202, p. 1-13, article nº 117259, Sept. 2022
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
FELIZARDO, Leonardo Kanashiro et al. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, v. 202, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2022.117259. Acesso em: 21 maio 2024. -
APA
Felizardo, L. K., Brandimarte, P., Del Moral Hernandez, E., Costa, A. H. R., Matsumoto, E. Y., Paiva, F. C. L., & Graves, C. de V. (2022). Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market. Expert Systems with Applications, 202, 1-13. doi:10.1016/j.eswa.2022.117259 -
NLM
Felizardo LK, Brandimarte P, Del Moral Hernandez E, Costa AHR, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ; 202 1-13.[citado 2024 maio 21 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259 -
Vancouver
Felizardo LK, Brandimarte P, Del Moral Hernandez E, Costa AHR, Matsumoto EY, Paiva FCL, Graves C de V. Outperforming algorithmic trading reinforcement learning systems: a supervised approach to the cryptocurrency market [Internet]. Expert Systems with Applications. 2022 ; 202 1-13.[citado 2024 maio 21 ] Available from: https://doi.org/10.1016/j.eswa.2022.117259 - Classificação de tumores e massas de mama utilizando um comitê de perceptrons de múltiplas camadas
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Informações sobre o DOI: 10.1016/j.eswa.2022.117259 (Fonte: oaDOI API)
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