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Trabalho de evento-anais periodico
Effective sample size, dimensionality, and generalization in covariate shift adaptation (2023)
Polo, Felipe Maia ; Vicente, Renato
Fonte: Neural Computing and Applications. Nome do evento: 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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-021-06615-1
Artigo de periodico
Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis (2023)
Vargas, Dionathan Luan de ; Oliva, Jefferson Tales ; Teixeira, Marcelo ; Casanova, Dalcimar ; Rosa, João Luís Garcia
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ELETROENCEFALOGRAFIA, EPILEPSIA, DIAGNÓSTICO POR COMPUTADOR, TECNOLOGIAS DA SAÚDE
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ABNT
VARGAS, Dionathan Luan de et al. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Computing and Applications, v. 35, n. 16, p. 12195-12219, 2023Tradução . . Disponível em: https://doi.org/10.1007/s00521-023-08350-1. Acesso em: 16 jun. 2025.
APA
Vargas, D. L. de, Oliva, J. T., Teixeira, M., Casanova, D., & Rosa, J. L. G. (2023). Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Computing and Applications, 35( 16), 12195-12219. doi:10.1007/s00521-023-08350-1
NLM
Vargas DL de, Oliva JT, Teixeira M, Casanova D, Rosa JLG. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis [Internet]. Neural Computing and Applications. 2023 ; 35( 16): 12195-12219.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-023-08350-1
Vancouver
Vargas DL de, Oliva JT, Teixeira M, Casanova D, Rosa JLG. Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis [Internet]. Neural Computing and Applications. 2023 ; 35( 16): 12195-12219.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-023-08350-1
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
Fonte: Neural Computing and Applications. Unidade: FEA
Assuntos: APRENDIZADO COMPUTACIONAL, LÓGICA FUZZY, MOEDA (ECONOMIA)
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
Fonte: 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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
Fonte: Neural Computing and Applications. Unidade: EACH
Assuntos: 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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] 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
Fonte: Neural Computing and Applications. Unidade: EACH
Assuntos: RECONHECIMENTO DE PADRÕES, INTELIGÊNCIA ARTIFICIAL, ANÁLISE DO MOVIMENTO HUMANO, ELETROMIOGRAFIA
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1953-5
Artigo de periodico
Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure (2016)
Messias, Valter Rogério; Estrella, Júlio Cezar ; Ehlers, Ricardo Sandes ; Santana, Marcos José; Santana, Regina Helena Carlucci; Reiff-Marganiec, Stephan
Fonte: Neural Computing and Applications. Unidade: ICMC
Assuntos: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE, INFERÊNCIA BAYESIANA, ESTATÍSTICA APLICADA, ALGORITMOS GENÉTICOS, MODELOS EM SÉRIES TEMPORAIS, ELASTICIDADE, COMPUTAÇÃO EM NUVEM
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MESSIAS, Valter Rogério et al. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, v. No 2016, n. 8, p. 2383-2406, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-2133-3. Acesso em: 16 jun. 2025.
APA
Messias, V. R., Estrella, J. C., Ehlers, R. S., Santana, M. J., Santana, R. H. C., & Reiff-Marganiec, S. (2016). Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, No 2016( 8), 2383-2406. doi:10.1007/s00521-015-2133-3
NLM
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
Vancouver
Messias VR, Estrella JC, Ehlers RS, Santana MJ, Santana RHC, Reiff-Marganiec S. Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure [Internet]. Neural Computing and Applications. 2016 ; No 2016( 8): 2383-2406.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-2133-3
Artigo de periodico
Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil (2016)
Furquim, Gustavo; Pessin, Gustavo; Faiçal, Bruno S; Mendiondo, Eduardo Mario ; Ueyama, Jó
Fonte: Neural Computing and Applications. Unidades: EESC, ICMC
Assuntos: SISTEMAS DISTRIBUÍDOS, PROGRAMAÇÃO CONCORRENTE
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ABNT
FURQUIM, Gustavo et al. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, v. 27, p. 1129-1141, 2016Tradução . . Disponível em: https://doi.org/10.1007/s00521-015-1930-z. Acesso em: 16 jun. 2025.
APA
Furquim, G., Pessin, G., Faiçal, B. S., Mendiondo, E. M., & Ueyama, J. (2016). Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil. Neural Computing and Applications, 27, 1129-1141. doi:10.1007/s00521-015-1930-z
NLM
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1930-z
Vancouver
Furquim G, Pessin G, Faiçal BS, Mendiondo EM, Ueyama J. Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory: a case study involving a real wireless sensor network deployment in Brazil [Internet]. Neural Computing and Applications. 2016 ; 27 1129-1141.[citado 2025 jun. 16 ] Available from: https://doi.org/10.1007/s00521-015-1930-z

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