ReP USP - Resultado da busca

Trabalho de evento
Label construction for multi-label feature selection (2014)
Spolaôr, Newton; Monard, Maria Carolina ; Tsoumakas, Grigorios; Lee, Huei Diana
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton et al. Label construction for multi-label feature selection. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.52. Acesso em: 28 nov. 2025.
APA
Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. D. (2014). Label construction for multi-label feature selection. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.52
NLM
Spolaôr N, Monard MC, Tsoumakas G, Lee HD. Label construction for multi-label feature selection [Internet]. Proceedings. 2014 ;[citado 2025 nov. 28 ] Available from: https://doi.org/10.1109/BRACIS.2014.52
Vancouver
Spolaôr N, Monard MC, Tsoumakas G, Lee HD. Label construction for multi-label feature selection [Internet]. Proceedings. 2014 ;[citado 2025 nov. 28 ] Available from: https://doi.org/10.1109/BRACIS.2014.52
Trabalho de evento-anais periodico
A framework to generate synthetic multi-label datasets (2014)
Tomás, Jimena Torres; Spolaôr, Newton; Cherman, Everton Alvares; Monard, Maria Carolina
Source: Electronic Notes in Theoretical Computer Science. Conference titles: Latin American Computing Conference - CLEI. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TOMÁS, Jimena Torres et al. A framework to generate synthetic multi-label datasets. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. Disponível em: https://doi.org/10.1016/j.entcs.2014.01.025. Acesso em: 28 nov. 2025. , 2014
APA
Tomás, J. T., Spolaôr, N., Cherman, E. A., & Monard, M. C. (2014). A framework to generate synthetic multi-label datasets. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. doi:10.1016/j.entcs.2014.01.025
NLM
Tomás JT, Spolaôr N, Cherman EA, Monard MC. A framework to generate synthetic multi-label datasets [Internet]. Electronic Notes in Theoretical Computer Science. 2014 ; fe 2014 155-176.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.entcs.2014.01.025
Vancouver
Tomás JT, Spolaôr N, Cherman EA, Monard MC. A framework to generate synthetic multi-label datasets [Internet]. Electronic Notes in Theoretical Computer Science. 2014 ; fe 2014 155-176.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.entcs.2014.01.025
Trabalho de evento-anais periodico
Evaluating ReliefF-based multi-label feature selection algorithm (2014)
Spolaôr, Newton; Monard, Maria Carolina
Source: Lecture Notes in Artifical Intelligence. Conference titles: Ibero-American Conference on Artificial Intelligence - IBERAMIA : Advances in Artificial Intelligence. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton e MONARD, Maria Carolina. Evaluating ReliefF-based multi-label feature selection algorithm. Lecture Notes in Artifical Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-12027-0_16. Acesso em: 28 nov. 2025. , 2014
APA
Spolaôr, N., & Monard, M. C. (2014). Evaluating ReliefF-based multi-label feature selection algorithm. Lecture Notes in Artifical Intelligence. Cham: Springer. doi:10.1007/978-3-319-12027-0_16
NLM
Spolaôr N, Monard MC. Evaluating ReliefF-based multi-label feature selection algorithm [Internet]. Lecture Notes in Artifical Intelligence. 2014 ; 8864 194-205.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-319-12027-0_16
Vancouver
Spolaôr N, Monard MC. Evaluating ReliefF-based multi-label feature selection algorithm [Internet]. Lecture Notes in Artifical Intelligence. 2014 ; 8864 194-205.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-319-12027-0_16
Trabalho de evento
A framework for multi-label exploratory data analysis: ML-EDA (2014)
Carvalho, Victor Augusto Moraes; Spolaôr, Newton; Cherman, Everton Alvares; Monard, Maria Carolina
Source: Proceedings. Conference titles: Latin American Computing Conference - CLEI. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, COMPUTAÇÃO GRÁFICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CARVALHO, Victor Augusto Moraes et al. A framework for multi-label exploratory data analysis: ML-EDA. 2014, Anais.. Red Hook: IEEE, 2014. Disponível em: https://doi.org/10.1109/CLEI.2014.6965166. Acesso em: 28 nov. 2025.
APA
Carvalho, V. A. M., Spolaôr, N., Cherman, E. A., & Monard, M. C. (2014). A framework for multi-label exploratory data analysis: ML-EDA. In Proceedings. Red Hook: IEEE. doi:10.1109/CLEI.2014.6965166
NLM
Carvalho VAM, Spolaôr N, Cherman EA, Monard MC. A framework for multi-label exploratory data analysis: ML-EDA [Internet]. Proceedings. 2014 ;[citado 2025 nov. 28 ] Available from: https://doi.org/10.1109/CLEI.2014.6965166
Vancouver
Carvalho VAM, Spolaôr N, Cherman EA, Monard MC. A framework for multi-label exploratory data analysis: ML-EDA [Internet]. Proceedings. 2014 ;[citado 2025 nov. 28 ] Available from: https://doi.org/10.1109/CLEI.2014.6965166

USP Schools

ReP

Filtros

Authors