Source: Neural Computing and Applications. Unidade: EACH
Subjects: RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA ARTIFICIAL, ANÁLISE DO MOVIMENTO HUMANO, ELETROMIOGRAFIA
ABNT
LIMA, Clodoaldo Aparecido de Moraes et al. Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, v. 27, n. 3, p. 791-804, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1953-5. Acesso em: 15 maio 2024.APA
Lima, C. A. de M., Coelho, A. L. V., Madeo, R. C. B., & Peres, S. M. (2016). Classification of electromyography signals using relevance vector machines and fractal dimension. Neural Computing and Applications, 27( 3), 791-804. doi:10.1007/s00521-015-1953-5NLM
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2024 maio 15 ] Available from: https://doi.org/10.1007/s00521-015-1953-5Vancouver
Lima CA de M, Coelho ALV, Madeo RCB, Peres SM. Classification of electromyography signals using relevance vector machines and fractal dimension [Internet]. Neural Computing and Applications. 2016 ; 27( 3): 791-804.[citado 2024 maio 15 ] Available from: https://doi.org/10.1007/s00521-015-1953-5