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Effective sample size, dimensionality, and generalization in covariate shift adaptation (2023)
Polo, Felipe Maia ; Vicente, Renato
Source: Neural Computing and Applications. Conference titles: LatinX in AI at NeurIPS. Unidade: IME
Assunto: MATEMÁTICA APLICADA
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ABNT
POLO, Felipe Maia e VICENTE, Renato. Effective sample size, dimensionality, and generalization in covariate shift adaptation. Neural Computing and Applications. Godalming: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s00521-021-06615-1. Acesso em: 21 nov. 2025. , 2023
APA
Polo, F. M., & Vicente, R. (2023). Effective sample size, dimensionality, and generalization in covariate shift adaptation. Neural Computing and Applications. Godalming: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1007/s00521-021-06615-1
NLM
Polo FM, Vicente R. Effective sample size, dimensionality, and generalization in covariate shift adaptation [Internet]. Neural Computing and Applications. 2023 ; 35( 25): 18187-18199.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-021-06615-1
Vancouver
Polo FM, Vicente R. Effective sample size, dimensionality, and generalization in covariate shift adaptation [Internet]. Neural Computing and Applications. 2023 ; 35( 25): 18187-18199.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-021-06615-1
Artigo de periodico
Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction (2021)
Maciel, Leandro dos Santos ; Ballini, Rosangela ; Gomide, Fernando
Source: Neural Computing and Applications. Unidade: FEA
Subjects: APRENDIZADO COMPUTACIONAL, LÓGICA FUZZY, MOEDA (ECONOMIA)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MACIEL, Leandro dos Santos e BALLINI, Rosangela e GOMIDE, Fernando. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction. Neural Computing and Applications, v. 1, p. 1, 2021Tradução . . Disponível em: https://doi.org/10.1007/s00521-021-06263-5. Acesso em: 21 nov. 2025.
APA
Maciel, L. dos S., Ballini, R., & Gomide, F. (2021). Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction. Neural Computing and Applications, 1, 1. doi:10.1007/s00521-021-06263-5
NLM
Maciel L dos S, Ballini R, Gomide F. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction [Internet]. Neural Computing and Applications. 2021 ; 1 1.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-021-06263-5
Vancouver
Maciel L dos S, Ballini R, Gomide F. Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction [Internet]. Neural Computing and Applications. 2021 ; 1 1.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-021-06263-5
Artigo de periodico
OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects (2018)
Sousa, Miguel Angelo de Abreu de; Del Moral Hernandez, Emilio ; Pires, Ricardo
Source: Neural Computing and Applications. Unidade: EP
Assunto: SISTEMAS EMBUTIDOS
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ABNT
SOUSA, Miguel Angelo de Abreu de e DEL MORAL HERNANDEZ, Emilio e PIRES, Ricardo. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, v. 30, n. 12, p. 3759-3771, 2018Tradução . . Disponível em: https://doi.org/10.1007/s00521-017-2957-0. Acesso em: 21 nov. 2025.
APA
Sousa, M. A. de A. de, Del Moral Hernandez, E., & Pires, R. (2018). OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects. Neural Computing and Applications, 30( 12), 3759-3771. doi:10.1007/s00521-017-2957-0
NLM
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
Vancouver
Sousa MA de A de, Del Moral Hernandez E, Pires R. OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects [Internet]. Neural Computing and Applications. 2018 ; 30( 12): 3759-3771.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-017-2957-0
Artigo de periodico
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms (2017)
Pereira, Luís Augusto Martins; Papa, João Paulo; Coelho, André Luís Vasconcelos; Lima, Clodoaldo Aparecido de Moraes; Pereira, Danillo Roberto; Albuquerque, Victor Hugo Costa de
Source: Neural Computing and Applications. Unidade: EACH
Subjects: EPILEPSIA, ELETROENCEFALOGRAFIA, APRENDIZADO COMPUTACIONAL
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ABNT
PEREIRA, Luís Augusto Martins et al. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. Neural Computing and Applications, n. ju 2017, p. 1-13, 2017Tradução . . Disponível em: https://doi.org/10.1007/s00521-017-3124-3. Acesso em: 21 nov. 2025.
APA
Pereira, L. A. M., Papa, J. P., Coelho, A. L. V., Lima, C. A. de M., Pereira, D. R., & Albuquerque, V. H. C. de. (2017). Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. Neural Computing and Applications, ( ju 2017), 1-13. doi:10.1007/s00521-017-3124-3
NLM
Pereira LAM, Papa JP, Coelho ALV, Lima CA de M, Pereira DR, Albuquerque VHC de. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms [Internet]. Neural Computing and Applications. 2017 ;( ju 2017): 1-13.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-017-3124-3
Vancouver
Pereira LAM, Papa JP, Coelho ALV, Lima CA de M, Pereira DR, Albuquerque VHC de. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms [Internet]. Neural Computing and Applications. 2017 ;( ju 2017): 1-13.[citado 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-017-3124-3
Artigo de periodico
Classification of electromyography signals using relevance vector machines and fractal dimension (2016)
Lima, Clodoaldo Aparecido de Moraes; Coelho, André L.V; Madeo, Renata Cristina Barros; Peres, Sarajane Marques
Source: Neural Computing and Applications. Unidade: EACH
Subjects: RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA ARTIFICIAL, ANÁLISE DO MOVIMENTO HUMANO, ELETROMIOGRAFIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 21 nov. 2025.
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/s00521-015-1953-5
NLM
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 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
Vancouver
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 2025 nov. 21 ] Available from: https://doi.org/10.1007/s00521-015-1953-5

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