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Trabalho de evento-anais periodico
Evaluating clustering meta-features for classifier recommendation (2021)
Garcia, Luís Paulo Faina ; Campelo, Felipe ; Ramos, Guilherme N ; Rivolli, Adriano ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, DESCOBERTA DE CONHECIMENTO, RECONHECIMENTO DE PADRÕES
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ABNT
GARCIA, Luís Paulo Faina et al. Evaluating clustering meta-features for classifier recommendation. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91702-9_30. Acesso em: 05 nov. 2025. , 2021
APA
Garcia, L. P. F., Campelo, F., Ramos, G. N., Rivolli, A., & Carvalho, A. C. P. de L. F. de. (2021). Evaluating clustering meta-features for classifier recommendation. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-91702-9_30
NLM
Garcia LPF, Campelo F, Ramos GN, Rivolli A, Carvalho ACP de LF de. Evaluating clustering meta-features for classifier recommendation [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13073 453-467.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_30
Vancouver
Garcia LPF, Campelo F, Ramos GN, Rivolli A, Carvalho ACP de LF de. Evaluating clustering meta-features for classifier recommendation [Internet]. Lecture Notes in Artificial Intelligence. 2021 ; 13073 453-467.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_30
Trabalho de evento-anais periodico
A fuzzy approach for classification and novelty detection in data streams under intermediate latency (2020)
Cristiani, André Luis ; Silva, Tiago Pinho da ; Camargo, Heloisa de Arruda
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, FUZZY (INTELIGÊNCIA ARTIFICIAL), RECONHECIMENTO DE PADRÕES, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
CRISTIANI, André Luis e SILVA, Tiago Pinho da e CAMARGO, Heloisa de Arruda. A fuzzy approach for classification and novelty detection in data streams under intermediate latency. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_12. Acesso em: 05 nov. 2025. , 2020
APA
Cristiani, A. L., Silva, T. P. da, & Camargo, H. de A. (2020). A fuzzy approach for classification and novelty detection in data streams under intermediate latency. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_12
NLM
Cristiani AL, Silva TP da, Camargo H de A. A fuzzy approach for classification and novelty detection in data streams under intermediate latency [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 171-186.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_12
Vancouver
Cristiani AL, Silva TP da, Camargo H de A. A fuzzy approach for classification and novelty detection in data streams under intermediate latency [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 171-186.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_12
Trabalho de evento-anais periodico
Simulating complexity measures on imbalanced datasets (2020)
Barella, Victor Hugo ; Garcia, Luís Paulo Faina ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS, RECONHECIMENTO DE PADRÕES, MATEMÁTICA DA COMPUTAÇÃO
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ABNT
BARELLA, Victor Hugo e GARCIA, Luís Paulo Faina e CARVALHO, André Carlos Ponce de Leon Ferreira de. Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_34. Acesso em: 05 nov. 2025. , 2020
APA
Barella, V. H., Garcia, L. P. F., & Carvalho, A. C. P. de L. F. de. (2020). Simulating complexity measures on imbalanced datasets. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-61380-8_34
NLM
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Vancouver
Barella VH, Garcia LPF, Carvalho ACP de LF de. Simulating complexity measures on imbalanced datasets [Internet]. Lecture Notes in Artificial Intelligence. 2020 ; 12320 498-512.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_34
Trabalho de evento-anais periodico
One-class quantification (2019)
Reis, Denis dos; Maletzke, André Gustavo; Cherman, Everton Alvares; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
REIS, Denis dos et al. One-class quantification. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-10925-7_17. Acesso em: 05 nov. 2025. , 2019
APA
Reis, D. dos, Maletzke, A. G., Cherman, E. A., & Batista, G. E. de A. P. A. (2019). One-class quantification. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-030-10925-7_17
NLM
Reis D dos, Maletzke AG, Cherman EA, Batista GE de APA. One-class quantification [Internet]. Lecture Notes in Artificial Intelligence. 2019 ; 11051 273-289.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-10925-7_17
Vancouver
Reis D dos, Maletzke AG, Cherman EA, Batista GE de APA. One-class quantification [Internet]. Lecture Notes in Artificial Intelligence. 2019 ; 11051 273-289.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-030-10925-7_17
Trabalho de evento-anais periodico
Food truck recommendation using multi-label classification (2017)
Rivolli, Adriano; Parker, Larissa C; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: EPIA Conference on Artificial Intelligence - EPIA. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES
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ABNT
RIVOLLI, Adriano e PARKER, Larissa C e CARVALHO, André Carlos Ponce de Leon Ferreira de. Food truck recommendation using multi-label classification. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-65340-2_48. Acesso em: 05 nov. 2025. , 2017
APA
Rivolli, A., Parker, L. C., & Carvalho, A. C. P. de L. F. de. (2017). Food truck recommendation using multi-label classification. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-65340-2_48
NLM
Rivolli A, Parker LC, Carvalho ACP de LF de. Food truck recommendation using multi-label classification [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10423 585-596.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-65340-2_48
Vancouver
Rivolli A, Parker LC, Carvalho ACP de LF de. Food truck recommendation using multi-label classification [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10423 585-596.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-65340-2_48
Trabalho de evento-resumo
Recommending collaborative filtering algorithms using subsampling landmarkers (2017)
Cunha, Tiago; Soares, Carlos; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Discovery Science - DS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES
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ABNT
CUNHA, Tiago e SOARES, Carlos e CARVALHO, André Carlos Ponce de Leon Ferreira de. Recommending collaborative filtering algorithms using subsampling landmarkers. 2017, Anais.. Cham: Springer, 2017. p. 189-203. Disponível em: https://doi.org/10.1007/978-3-319-67786-6_14. Acesso em: 05 nov. 2025.
APA
Cunha, T., Soares, C., & Carvalho, A. C. P. de L. F. de. (2017). Recommending collaborative filtering algorithms using subsampling landmarkers. In Lecture Notes in Artificial Intelligence (Vol. 10558, p. 189-203). Cham: Springer. doi:10.1007/978-3-319-67786-6_14
NLM
Cunha T, Soares C, Carvalho ACP de LF de. Recommending collaborative filtering algorithms using subsampling landmarkers [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10558 189-203.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-67786-6_14
Vancouver
Cunha T, Soares C, Carvalho ACP de LF de. Recommending collaborative filtering algorithms using subsampling landmarkers [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10558 189-203.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-67786-6_14
Trabalho de evento-anais periodico
Predictive models for differentiation between normal and abnormal EEG through cross-correlation and machine learning techniques (2017)
Oliva, Jefferson Tales; Rosa, João Luís Garcia
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Banff International Research Station for Mathematical Innovation and Discovery Workshop - BIRS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, RECONHECIMENTO DE PADRÕES, ELETROENCEFALOGRAFIA, EPILEPSIA
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ABNT
OLIVA, Jefferson Tales e ROSA, João Luís Garcia. Predictive models for differentiation between normal and abnormal EEG through cross-correlation and machine learning techniques. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-69775-8_7. Acesso em: 05 nov. 2025. , 2017
APA
Oliva, J. T., & Rosa, J. L. G. (2017). Predictive models for differentiation between normal and abnormal EEG through cross-correlation and machine learning techniques. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-69775-8_7
NLM
Oliva JT, Rosa JLG. Predictive models for differentiation between normal and abnormal EEG through cross-correlation and machine learning techniques [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10344 134-145.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-69775-8_7
Vancouver
Oliva JT, Rosa JLG. Predictive models for differentiation between normal and abnormal EEG through cross-correlation and machine learning techniques [Internet]. Lecture Notes in Artificial Intelligence. 2017 ; 10344 134-145.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-69775-8_7
Trabalho de evento-anais periodico
Selecting collaborative filtering algorithms using metalearning (2016)
Cunha, Tiago; Soares, Carlos; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD. Unidade: ICMC
Assuntos: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
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ABNT
CUNHA, Tiago e SOARES, Carlos e CARVALHO, André Carlos Ponce de Leon Ferreira de. Selecting collaborative filtering algorithms using metalearning. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-46227-1_25. Acesso em: 05 nov. 2025. , 2016
APA
Cunha, T., Soares, C., & Carvalho, A. C. P. de L. F. de. (2016). Selecting collaborative filtering algorithms using metalearning. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-46227-1_25
NLM
Cunha T, Soares C, Carvalho ACP de LF de. Selecting collaborative filtering algorithms using metalearning [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 393-409.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_25
Vancouver
Cunha T, Soares C, Carvalho ACP de LF de. Selecting collaborative filtering algorithms using metalearning [Internet]. Lecture Notes in Artificial Intelligence. 2016 ; 9852 393-409.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-319-46227-1_25
Trabalho de evento-anais periodico
Empirical evaluation of ranking prediction methods for gene expression data classification (2010)
Souza, Bruno Feres de; Carvalho, André Carlos Ponce de Leon Ferreira de ; Soares, Carlos
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: Ibero-American Conference on AI - IBERAMIA. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
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ABNT
SOUZA, Bruno Feres de e CARVALHO, André Carlos Ponce de Leon Ferreira de e SOARES, Carlos. Empirical evaluation of ranking prediction methods for gene expression data classification. Lecture Notes in Artificial Intelligence. Heidelberg: Springer. Disponível em: https://doi.org/10.1007/978-3-642-16952-6_20. Acesso em: 05 nov. 2025. , 2010
APA
Souza, B. F. de, Carvalho, A. C. P. de L. F. de, & Soares, C. (2010). Empirical evaluation of ranking prediction methods for gene expression data classification. Lecture Notes in Artificial Intelligence. Heidelberg: Springer. doi:10.1007/978-3-642-16952-6_20
NLM
Souza BF de, Carvalho ACP de LF de, Soares C. Empirical evaluation of ranking prediction methods for gene expression data classification [Internet]. Lecture Notes in Artificial Intelligence. 2010 ; 6433 194-203.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-16952-6_20
Vancouver
Souza BF de, Carvalho ACP de LF de, Soares C. Empirical evaluation of ranking prediction methods for gene expression data classification [Internet]. Lecture Notes in Artificial Intelligence. 2010 ; 6433 194-203.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-16952-6_20
Trabalho de evento-anais periodico
Use of classification algorithms in noise detection and elimination (2009)
Miranda, André L. B; Garcia, Luís Paulo F; Carvalho, André Carlos Ponce de Leon Ferreira de ; Lorena, Ana Carolina
Fonte: Lecture Notes in Artificial Intelligence. Nome do evento: International Conference on Hybrid Artificial Intelligence Systems - HAIS. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, RECONHECIMENTO DE PADRÕES
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ABNT
MIRANDA, André L. B et al. Use of classification algorithms in noise detection and elimination. Lecture Notes in Artificial Intelligence. Berlin: Springer. Disponível em: https://doi.org/10.1007/978-3-642-02319-4_50. Acesso em: 05 nov. 2025. , 2009
APA
Miranda, A. L. B., Garcia, L. P. F., Carvalho, A. C. P. de L. F. de, & Lorena, A. C. (2009). Use of classification algorithms in noise detection and elimination. Lecture Notes in Artificial Intelligence. Berlin: Springer. doi:10.1007/978-3-642-02319-4_50
NLM
Miranda ALB, Garcia LPF, Carvalho ACP de LF de, Lorena AC. Use of classification algorithms in noise detection and elimination [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5572 417-424.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-02319-4_50
Vancouver
Miranda ALB, Garcia LPF, Carvalho ACP de LF de, Lorena AC. Use of classification algorithms in noise detection and elimination [Internet]. Lecture Notes in Artificial Intelligence. 2009 ; 5572 417-424.[citado 2025 nov. 05 ] Available from: https://doi.org/10.1007/978-3-642-02319-4_50

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