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Artigo de periodico
A systematic review of multi-label feature selection and a new method based on label construction (2016)
Spolaôr, Newton; Monard, Maria Carolina; Tsoumakas, Grigorios; Lee, Huei Diana
Source: Neurocomputing. 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. A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, v. 180, p. 3-15, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.neucom.2015.07.118. Acesso em: 26 maio 2024.
APA
Spolaôr, N., Monard, M. C., Tsoumakas, G., & Lee, H. D. (2016). A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, 180, 3-15. doi:10.1016/j.neucom.2015.07.118
NLM
Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
Vancouver
Spolaôr N, Monard MC, Tsoumakas G, Lee HD. A systematic review of multi-label feature selection and a new method based on label construction [Internet]. Neurocomputing. 2016 ; 180 3-15.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.neucom.2015.07.118
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: 26 maio 2024.
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 2024 maio 26 ] 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 2024 maio 26 ] Available from: https://doi.org/10.1109/BRACIS.2014.52
Parte de monografia/livro
Time series classification with motifs and characteristics (2014)
Maletzke, André Gustavo; Lee, Huei Diana; Batista, Gustavo Enrique de Almeida Prado Alves; Coy, Claudio Saddy Rodrigues; Fagundes, João José; Chung, Wu Feng
Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MALETZKE, André Gustavo et al. Time series classification with motifs and characteristics. Tradução . Heidelberg: Springer-Verlag, 2014. . . Acesso em: 26 maio 2024.
APA
Maletzke, A. G., Lee, H. D., Batista, G. E. de A. P. A., Coy, C. S. R., Fagundes, J. J., & Chung, W. F. (2014). Time series classification with motifs and characteristics. In . Heidelberg: Springer-Verlag.
NLM
Maletzke AG, Lee HD, Batista GE de APA, Coy CSR, Fagundes JJ, Chung WF. Time series classification with motifs and characteristics. Heidelberg: Springer-Verlag; 2014. [citado 2024 maio 26 ]
Vancouver
Maletzke AG, Lee HD, Batista GE de APA, Coy CSR, Fagundes JJ, Chung WF. Time series classification with motifs and characteristics. Heidelberg: Springer-Verlag; 2014. [citado 2024 maio 26 ]
Trabalho de evento
ReliefF for multi-label feature selection (2013)
Spolaôr, Newton; Cherman, Everton Alvares; Monard, Maria Carolina; Lee, Huei Diana
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton et al. ReliefF for multi-label feature selection. 2013, Anais.. Los Alamitos: Conference Publishing Services, 2013. Disponível em: https://doi.org/10.1109/BRACIS.2013.10. Acesso em: 26 maio 2024.
APA
Spolaôr, N., Cherman, E. A., Monard, M. C., & Lee, H. D. (2013). ReliefF for multi-label feature selection. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2013.10
NLM
Spolaôr N, Cherman EA, Monard MC, Lee HD. ReliefF for multi-label feature selection [Internet]. Proceedings. 2013 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1109/BRACIS.2013.10
Vancouver
Spolaôr N, Cherman EA, Monard MC, Lee HD. ReliefF for multi-label feature selection [Internet]. Proceedings. 2013 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1109/BRACIS.2013.10
Trabalho de evento-anais periodico
A comparison of multi-label feature selection methods using the problem transformation approach (2013)
Spolaôr, Newton; Cherman, Everton Alvares; Monard, Maria Carolina; Lee, Huei Diana
Source: Electronic Notes in Theoretical Computer Science. Conference titles: Latin American Conference in Informatics - CLEI. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton et al. A comparison of multi-label feature selection methods using the problem transformation approach. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. Disponível em: https://doi.org/10.1016/j.entcs.2013.02.010. Acesso em: 26 maio 2024. , 2013
APA
Spolaôr, N., Cherman, E. A., Monard, M. C., & Lee, H. D. (2013). A comparison of multi-label feature selection methods using the problem transformation approach. Electronic Notes in Theoretical Computer Science. Amsterdam: Elsevier. doi:10.1016/j.entcs.2013.02.010
NLM
Spolaôr N, Cherman EA, Monard MC, Lee HD. A comparison of multi-label feature selection methods using the problem transformation approach [Internet]. Electronic Notes in Theoretical Computer Science. 2013 ; 292( 5): 135-151.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.entcs.2013.02.010
Vancouver
Spolaôr N, Cherman EA, Monard MC, Lee HD. A comparison of multi-label feature selection methods using the problem transformation approach [Internet]. Electronic Notes in Theoretical Computer Science. 2013 ; 292( 5): 135-151.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.entcs.2013.02.010
Trabalho de evento-anais periodico
Filter approach feature selection methods to support multi-label learning based on ReliefF and information gain (2012)
Spolaôr, Newton; Cherman, Everton Alvares; Monard, Maria Carolina; Lee, Huei Diana
Source: Lecture Notes in Artificial Intelligence. Conference titles: Brazilian Symposium on Artificial Intelligence : Advances in Artificial Intelligence - SBIA. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SPOLAÔR, Newton et al. Filter approach feature selection methods to support multi-label learning based on ReliefF and information gain. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-34459-6_8. Acesso em: 26 maio 2024. , 2012
APA
Spolaôr, N., Cherman, E. A., Monard, M. C., & Lee, H. D. (2012). Filter approach feature selection methods to support multi-label learning based on ReliefF and information gain. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. doi:10.1007/978-3-642-34459-6_8
NLM
Spolaôr N, Cherman EA, Monard MC, Lee HD. Filter approach feature selection methods to support multi-label learning based on ReliefF and information gain [Internet]. Lecture Notes in Artificial Intelligence. 2012 ; 7589 72-81.[citado 2024 maio 26 ] Available from: https://doi.org/10.1007/978-3-642-34459-6_8
Vancouver
Spolaôr N, Cherman EA, Monard MC, Lee HD. Filter approach feature selection methods to support multi-label learning based on ReliefF and information gain [Internet]. Lecture Notes in Artificial Intelligence. 2012 ; 7589 72-81.[citado 2024 maio 26 ] Available from: https://doi.org/10.1007/978-3-642-34459-6_8

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