ReP USP - Resultado da busca

Artigo de periodico
Decomposing time series into deterministic and stochastic influences: a survey (2019)
Duarte, Felipe Simões Lage Gomes; Rios, Ricardo A; Hruschka, Eduardo Raul ; Mello, Rodrigo Fernandes de
Source: Digital Signal Processing. Unidades: EP, ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, MÉTODOS DE DECOMPOSIÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DUARTE, Felipe Simões Lage Gomes et al. Decomposing time series into deterministic and stochastic influences: a survey. Digital Signal Processing, v. 95, p. 1-18, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.dsp.2019.102582. Acesso em: 01 nov. 2024.
APA
Duarte, F. S. L. G., Rios, R. A., Hruschka, E. R., & Mello, R. F. de. (2019). Decomposing time series into deterministic and stochastic influences: a survey. Digital Signal Processing, 95, 1-18. doi:10.1016/j.dsp.2019.102582
NLM
Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Decomposing time series into deterministic and stochastic influences: a survey [Internet]. Digital Signal Processing. 2019 ; 95 1-18.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.dsp.2019.102582
Vancouver
Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Decomposing time series into deterministic and stochastic influences: a survey [Internet]. Digital Signal Processing. 2019 ; 95 1-18.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.dsp.2019.102582
Trabalho de evento
Time series decomposition using spring system applied on phase spaces (2018)
Duarte, Felipe Simões Lage Gomes; Rios, Ricardo A; Hruschka, Eduardo Raul ; Mello, Rodrigo Fernandes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: EP, ICMC
Subjects: MODELOS EM SÉRIES TEMPORAIS, PROCESSOS ESTOCÁSTICOS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DUARTE, Felipe Simões Lage Gomes et al. Time series decomposition using spring system applied on phase spaces. 2018, Anais.. Piscataway: IEEE, 2018. Disponível em: https://doi.org/10.1109/BRACIS.2018.00093. Acesso em: 01 nov. 2024.
APA
Duarte, F. S. L. G., Rios, R. A., Hruschka, E. R., & Mello, R. F. de. (2018). Time series decomposition using spring system applied on phase spaces. In Proceedings. Piscataway: IEEE. doi:10.1109/BRACIS.2018.00093
NLM
Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Time series decomposition using spring system applied on phase spaces [Internet]. Proceedings. 2018 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRACIS.2018.00093
Vancouver
Duarte FSLG, Rios RA, Hruschka ER, Mello RF de. Time series decomposition using spring system applied on phase spaces [Internet]. Proceedings. 2018 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRACIS.2018.00093
Artigo de periodico
An evolutionary algorithm for clustering data streams with a variable number of clusters (2017)
Silva, Jonathan de Andrade; Hruschka, Eduardo Raul ; Gama, João
Source: Expert Systems with Applications. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul e GAMA, João. An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Systems with Applications, v. 67, n. Ja 2017, p. 228-238, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2016.09.020. Acesso em: 01 nov. 2024.
APA
Silva, J. de A., Hruschka, E. R., & Gama, J. (2017). An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Systems with Applications, 67( Ja 2017), 228-238. doi:10.1016/j.eswa.2016.09.020
NLM
Silva J de A, Hruschka ER, Gama J. An evolutionary algorithm for clustering data streams with a variable number of clusters [Internet]. Expert Systems with Applications. 2017 ; 67( Ja 2017): 228-238.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.eswa.2016.09.020
Vancouver
Silva J de A, Hruschka ER, Gama J. An evolutionary algorithm for clustering data streams with a variable number of clusters [Internet]. Expert Systems with Applications. 2017 ; 67( Ja 2017): 228-238.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.eswa.2016.09.020
Artigo de periodico
A support system for clustering data streams with a variable number of clusters (2016)
Silva, Jonathan de Andrade; Hruschka, Eduardo Raul
Source: ACM Transactions on Autonomous and Adaptive Systems. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul. A support system for clustering data streams with a variable number of clusters. ACM Transactions on Autonomous and Adaptive Systems, v. 11, n. 2, p. 11:1-11:26, 2016Tradução . . Disponível em: https://doi.org/10.1145/2932704. Acesso em: 01 nov. 2024.
APA
Silva, J. de A., & Hruschka, E. R. (2016). A support system for clustering data streams with a variable number of clusters. ACM Transactions on Autonomous and Adaptive Systems, 11( 2), 11:1-11:26. doi:10.1145/2932704
NLM
Silva J de A, Hruschka ER. A support system for clustering data streams with a variable number of clusters [Internet]. ACM Transactions on Autonomous and Adaptive Systems. 2016 ; 11( 2): 11:1-11:26.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1145/2932704
Vancouver
Silva J de A, Hruschka ER. A support system for clustering data streams with a variable number of clusters [Internet]. ACM Transactions on Autonomous and Adaptive Systems. 2016 ; 11( 2): 11:1-11:26.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1145/2932704
Artigo de periodico
Evolving Gaussian mixture models with splitting and merging mutation operators (2016)
Covões, Thiago Ferreira; Hruschka, Eduardo Raul ; Ghosh, Joydeep
Source: Evolutionary Computation. Unidade: ICMC
Subjects: COMPUTAÇÃO EVOLUTIVA, ALGORITMOS GENÉTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. Evolving Gaussian mixture models with splitting and merging mutation operators. Evolutionary Computation, v. 24, n. 2, p. 293-317, 2016Tradução . . Disponível em: https://doi.org/10.1162/EVCO_a_00152. Acesso em: 01 nov. 2024.
APA
Covões, T. F., Hruschka, E. R., & Ghosh, J. (2016). Evolving Gaussian mixture models with splitting and merging mutation operators. Evolutionary Computation, 24( 2), 293-317. doi:10.1162/EVCO_a_00152
NLM
Covões TF, Hruschka ER, Ghosh J. Evolving Gaussian mixture models with splitting and merging mutation operators [Internet]. Evolutionary Computation. 2016 ; 24( 2): 293-317.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1162/EVCO_a_00152
Vancouver
Covões TF, Hruschka ER, Ghosh J. Evolving Gaussian mixture models with splitting and merging mutation operators [Internet]. Evolutionary Computation. 2016 ; 24( 2): 293-317.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1162/EVCO_a_00152
Artigo de periodico
Using metaheuristics to optimize the combination of classifier and cluster ensembles (2015)
Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep
Source: Integrated Computer-Aided Engineering. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLETTA, Luiz Fernando Sommaggio et al. Using metaheuristics to optimize the combination of classifier and cluster ensembles. Integrated Computer-Aided Engineering, v. 22, n. 3, p. 229-242, 2015Tradução . . Disponível em: https://doi.org/10.3233/ICA-150485. Acesso em: 01 nov. 2024.
APA
Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2015). Using metaheuristics to optimize the combination of classifier and cluster ensembles. Integrated Computer-Aided Engineering, 22( 3), 229-242. doi:10.3233/ICA-150485
NLM
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Using metaheuristics to optimize the combination of classifier and cluster ensembles [Internet]. Integrated Computer-Aided Engineering. 2015 ; 22( 3): 229-242.[citado 2024 nov. 01 ] Available from: https://doi.org/10.3233/ICA-150485
Vancouver
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Using metaheuristics to optimize the combination of classifier and cluster ensembles [Internet]. Integrated Computer-Aided Engineering. 2015 ; 22( 3): 229-242.[citado 2024 nov. 01 ] Available from: https://doi.org/10.3233/ICA-150485
Artigo de periodico
Interactive textual feature selection for consensus clustering (2015)
Corrêa, Geraldo N; Marcacini, Ricardo Marcondes ; Hruschka, Eduardo Raul ; Rezende, Solange Oliveira
Source: Pattern Recognition Letters. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CORRÊA, Geraldo N et al. Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, v. 52, n. ja 2015, p. 25-31, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.patrec.2014.09.008. Acesso em: 01 nov. 2024.
APA
Corrêa, G. N., Marcacini, R. M., Hruschka, E. R., & Rezende, S. O. (2015). Interactive textual feature selection for consensus clustering. Pattern Recognition Letters, 52( ja 2015), 25-31. doi:10.1016/j.patrec.2014.09.008
NLM
Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008
Vancouver
Corrêa GN, Marcacini RM, Hruschka ER, Rezende SO. Interactive textual feature selection for consensus clustering [Internet]. Pattern Recognition Letters. 2015 ; 52( ja 2015): 25-31.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.patrec.2014.09.008
Artigo de periodico
A differential evolution algorithm to optimise the combination of classifier and cluster ensembles (2015)
Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep
Source: International Journal of Bio-Inspired Computation. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLETTA, Luiz Fernando Sommaggio et al. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles. International Journal of Bio-Inspired Computation, v. 7, n. 2, p. 111-124, 2015Tradução . . Disponível em: https://doi.org/10.1504/IJBIC.2015.069288. Acesso em: 01 nov. 2024.
APA
Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2015). A differential evolution algorithm to optimise the combination of classifier and cluster ensembles. International Journal of Bio-Inspired Computation, 7( 2), 111-124. doi:10.1504/IJBIC.2015.069288
NLM
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles [Internet]. International Journal of Bio-Inspired Computation. 2015 ; 7( 2): 111-124.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1504/IJBIC.2015.069288
Vancouver
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. A differential evolution algorithm to optimise the combination of classifier and cluster ensembles [Internet]. International Journal of Bio-Inspired Computation. 2015 ; 7( 2): 111-124.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1504/IJBIC.2015.069288
Parte de monografia/livro
Mineração de dados para identificar atributos genéticos associados à características de interesse econômico à pecuária (2014)
Gonzaga, André; Mudadu, Maurício de A; Higa, Roberto H; Hruschka, Eduardo Raul
Source: Talking about computing and genomic (TACG) : modelos e métodos computacionais em bioinformática. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS, MELHORAMENTO GENÉTICO ANIMAL, PECUÁRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GONZAGA, André et al. Mineração de dados para identificar atributos genéticos associados à características de interesse econômico à pecuária. Talking about computing and genomic (TACG) : modelos e métodos computacionais em bioinformática. Tradução . Brasília: Embrapa, 2014. . . Acesso em: 01 nov. 2024.
APA
Gonzaga, A., Mudadu, M. de A., Higa, R. H., & Hruschka, E. R. (2014). Mineração de dados para identificar atributos genéticos associados à características de interesse econômico à pecuária. In Talking about computing and genomic (TACG) : modelos e métodos computacionais em bioinformática. Brasília: Embrapa.
NLM
Gonzaga A, Mudadu M de A, Higa RH, Hruschka ER. Mineração de dados para identificar atributos genéticos associados à características de interesse econômico à pecuária. In: Talking about computing and genomic (TACG) : modelos e métodos computacionais em bioinformática. Brasília: Embrapa; 2014. [citado 2024 nov. 01 ]
Vancouver
Gonzaga A, Mudadu M de A, Higa RH, Hruschka ER. Mineração de dados para identificar atributos genéticos associados à características de interesse econômico à pecuária. In: Talking about computing and genomic (TACG) : modelos e métodos computacionais em bioinformática. Brasília: Embrapa; 2014. [citado 2024 nov. 01 ]
Trabalho de evento
Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble (2014)
Silva, Nádia Felix Felipe da; Hruschka, Eduardo Raul ; Hruschka Junior, Estevam Rafael
Source: Proceedings. Conference titles: International Workshop on Semantic Evaluation - SemEval. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, WEB SEMÂNTICA, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Nádia Felix Felipe da e HRUSCHKA, Eduardo Raul e HRUSCHKA JUNIOR, Estevam Rafael. Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble. 2014, Anais.. Stroudsburg: ACL, 2014. Disponível em: http://www.aclweb.org/anthology/S/S14/S14-2017.pdf. Acesso em: 01 nov. 2024.
APA
Silva, N. F. F. da, Hruschka, E. R., & Hruschka Junior, E. R. (2014). Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble. In Proceedings. Stroudsburg: ACL. Recuperado de http://www.aclweb.org/anthology/S/S14/S14-2017.pdf
NLM
Silva NFF da, Hruschka ER, Hruschka Junior ER. Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble [Internet]. Proceedings. 2014 ;[citado 2024 nov. 01 ] Available from: http://www.aclweb.org/anthology/S/S14/S14-2017.pdf
Vancouver
Silva NFF da, Hruschka ER, Hruschka Junior ER. Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble [Internet]. Proceedings. 2014 ;[citado 2024 nov. 01 ] Available from: http://www.aclweb.org/anthology/S/S14/S14-2017.pdf
Trabalho de evento
Combining classification and clustering for tweet sentiment analysis (2014)
Coletta, Luiz Fernando Sommaggio ; Silva, Nádia Felix Felipe da; Hruschka, Eduardo Raul ; Hruschka Junior, Estevam Rafael
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, WEB SEMÂNTICA, MÍDIAS SOCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLETTA, Luiz Fernando Sommaggio et al. Combining classification and clustering for tweet sentiment analysis. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.46. Acesso em: 01 nov. 2024.
APA
Coletta, L. F. S., Silva, N. F. F. da, Hruschka, E. R., & Hruschka Junior, E. R. (2014). Combining classification and clustering for tweet sentiment analysis. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.46
NLM
Coletta LFS, Silva NFF da, Hruschka ER, Hruschka Junior ER. Combining classification and clustering for tweet sentiment analysis [Internet]. Proceedings. 2014 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRACIS.2014.46
Vancouver
Coletta LFS, Silva NFF da, Hruschka ER, Hruschka Junior ER. Combining classification and clustering for tweet sentiment analysis [Internet]. Proceedings. 2014 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRACIS.2014.46
Artigo de periodico
An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning (2014)
Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Acharyya, Sreangsu
Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS, VISÃO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ACHARYA, Ayan et al. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, v. 9, n. 1, p. 1:1-1:35, 2014Tradução . . Disponível em: https://doi.org/10.1145/2601435. Acesso em: 01 nov. 2024.
APA
Acharya, A., Hruschka, E. R., Ghosh, J., & Acharyya, S. (2014). An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, 9( 1), 1:1-1:35. doi:10.1145/2601435
NLM
Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1145/2601435
Vancouver
Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1145/2601435
Artigo de periodico
Tweet sentiment analysis with classifier ensembles (2014)
Silva, Nádia Felix Felipe da; Hruschka, Eduardo Raul ; Hruschka Junior, Estevam Rafael
Source: Decision Support Systems. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Nádia Felix Felipe da e HRUSCHKA, Eduardo Raul e HRUSCHKA JUNIOR, Estevam Rafael. Tweet sentiment analysis with classifier ensembles. Decision Support Systems, v. 66, p. 170-179, 2014Tradução . . Disponível em: https://doi.org/10.1016/j.dss.2014.07.003. Acesso em: 01 nov. 2024.
APA
Silva, N. F. F. da, Hruschka, E. R., & Hruschka Junior, E. R. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170-179. doi:10.1016/j.dss.2014.07.003
NLM
Silva NFF da, Hruschka ER, Hruschka Junior ER. Tweet sentiment analysis with classifier ensembles [Internet]. Decision Support Systems. 2014 ; 66 170-179.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.dss.2014.07.003
Vancouver
Silva NFF da, Hruschka ER, Hruschka Junior ER. Tweet sentiment analysis with classifier ensembles [Internet]. Decision Support Systems. 2014 ; 66 170-179.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.dss.2014.07.003
Trabalho de evento-anais periodico
Hierarchical bottom-up safe semi-supervised support vector machines for multi-class transductive learning (2013)
Covões, Thiago F; Barros, Rodrigo C; Silva, Tiago S. da; Hruschka, Eduardo Raul ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Journal of Information and Data Management - JIDM. Conference titles: Brazilian Symposium on Databases - SBBD. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COVÕES, Thiago F et al. Hierarchical bottom-up safe semi-supervised support vector machines for multi-class transductive learning. Journal of Information and Data Management - JIDM. Porto Alegre: SBC. Disponível em: http://seer.lcc.ufmg.br/index.php/jidm/article/view/256. Acesso em: 01 nov. 2024. , 2013
APA
Covões, T. F., Barros, R. C., Silva, T. S. da, Hruschka, E. R., & Carvalho, A. C. P. de L. F. de. (2013). Hierarchical bottom-up safe semi-supervised support vector machines for multi-class transductive learning. Journal of Information and Data Management - JIDM. Porto Alegre: SBC. Recuperado de http://seer.lcc.ufmg.br/index.php/jidm/article/view/256
NLM
Covões TF, Barros RC, Silva TS da, Hruschka ER, Carvalho ACP de LF de. Hierarchical bottom-up safe semi-supervised support vector machines for multi-class transductive learning [Internet]. Journal of Information and Data Management - JIDM. 2013 ; 4( 3): 357-372.[citado 2024 nov. 01 ] Available from: http://seer.lcc.ufmg.br/index.php/jidm/article/view/256
Vancouver
Covões TF, Barros RC, Silva TS da, Hruschka ER, Carvalho ACP de LF de. Hierarchical bottom-up safe semi-supervised support vector machines for multi-class transductive learning [Internet]. Journal of Information and Data Management - JIDM. 2013 ; 4( 3): 357-372.[citado 2024 nov. 01 ] Available from: http://seer.lcc.ufmg.br/index.php/jidm/article/view/256
Artigo de periodico
Competitive learning with pairwise constraints (2013)
Covões, Thiago F; Hruschka, Eduardo Raul ; Ghosh, Joydeep
Source: IEEE Transactions on Neural Networks and Learning Systems. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COVÕES, Thiago F e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. Competitive learning with pairwise constraints. IEEE Transactions on Neural Networks and Learning Systems, v. 24, n. ja 2013, p. 164-169, 2013Tradução . . Disponível em: https://doi.org/10.1109/TNNLS.2012.2227064. Acesso em: 01 nov. 2024.
APA
Covões, T. F., Hruschka, E. R., & Ghosh, J. (2013). Competitive learning with pairwise constraints. IEEE Transactions on Neural Networks and Learning Systems, 24( ja 2013), 164-169. doi:10.1109/TNNLS.2012.2227064
NLM
Covões TF, Hruschka ER, Ghosh J. Competitive learning with pairwise constraints [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2013 ; 24( ja 2013): 164-169.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/TNNLS.2012.2227064
Vancouver
Covões TF, Hruschka ER, Ghosh J. Competitive learning with pairwise constraints [Internet]. IEEE Transactions on Neural Networks and Learning Systems. 2013 ; 24( ja 2013): 164-169.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/TNNLS.2012.2227064
Trabalho de evento
Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles (2013)
Coletta, Luiz Fernando Sommaggio ; Hruschka, Eduardo Raul ; Acharya, Ayan; Ghosh, Joydeep
Source: Proceedings. Conference titles: BRICS Countries Congress on Computational Intelligence - BRICS-CCI. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COLETTA, Luiz Fernando Sommaggio et al. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles. 2013, Anais.. Piscataway: IEEE, 2013. Disponível em: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86. Acesso em: 01 nov. 2024.
APA
Coletta, L. F. S., Hruschka, E. R., Acharya, A., & Ghosh, J. (2013). Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles. In Proceedings. Piscataway: IEEE. doi:10.1109/BRICS-CCI-CBIC.2013.86
NLM
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86
Vancouver
Coletta LFS, Hruschka ER, Acharya A, Ghosh J. Towards the use of metaheuristics for optimizing the combination of classifier and cluster ensembles [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.86
Artigo de periodico
An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks (2013)
Silva, Jonathan de Andrade; Hruschka, Eduardo Raul
Source: Data & Knowledge Engineering. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks. Data & Knowledge Engineering, v. 84, p. 47-58, 2013Tradução . . Disponível em: https://doi.org/10.1016/j.datak.2012.12.006. Acesso em: 01 nov. 2024.
APA
Silva, J. de A., & Hruschka, E. R. (2013). An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks. Data & Knowledge Engineering, 84, 47-58. doi:10.1016/j.datak.2012.12.006
NLM
Silva J de A, Hruschka ER. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks [Internet]. Data & Knowledge Engineering. 2013 ; 84 47-58.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.datak.2012.12.006
Vancouver
Silva J de A, Hruschka ER. An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks [Internet]. Data & Knowledge Engineering. 2013 ; 84 47-58.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1016/j.datak.2012.12.006
Trabalho de evento
Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm (2013)
Covões, Thiago F; Hruschka, Eduardo Raul
Source: Proceedings. Conference titles: IEEE Congress on Evolutionary Computation - CEC. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COVÕES, Thiago F e HRUSCHKA, Eduardo Raul. Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm. 2013, Anais.. Piscataway: IEEE, 2013. Disponível em: https://doi.org/10.1109/CEC.2013.6557962. Acesso em: 01 nov. 2024.
APA
Covões, T. F., & Hruschka, E. R. (2013). Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm. In Proceedings. Piscataway: IEEE. doi:10.1109/CEC.2013.6557962
NLM
Covões TF, Hruschka ER. Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/CEC.2013.6557962
Vancouver
Covões TF, Hruschka ER. Unsupervised learning of Gaussian mixture models: evolutionary create and eliminate for expectation maximization algorithm [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1109/CEC.2013.6557962
Artigo de periodico
A study of K-means-based algorithms for constrained clustering (2013)
Covões, Thiago Ferreira; Hruschka, Eduardo Raul ; Ghosh, Joydeep
Source: Intelligent Data Analysis. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, v. 17, n. 3, p. 485-505, 2013Tradução . . Disponível em: https://doi.org/10.3233/IDA-130590. Acesso em: 01 nov. 2024.
APA
Covões, T. F., Hruschka, E. R., & Ghosh, J. (2013). A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, 17( 3), 485-505. doi:10.3233/IDA-130590
NLM
Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2024 nov. 01 ] Available from: https://doi.org/10.3233/IDA-130590
Vancouver
Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2024 nov. 01 ] Available from: https://doi.org/10.3233/IDA-130590
Trabalho de evento
Probabilistic combination of classifier and cluster ensembles for non-transductive learning (2013)
Acharya, Ayan; Hruschka, Eduardo Raul ; Ghosh, Joydeep; Sarwar, Badrul; Ruvini, Jean-David
Source: Proceedings. Conference titles: SIAM International Conference on Data Mining. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ACHARYA, Ayan et al. Probabilistic combination of classifier and cluster ensembles for non-transductive learning. 2013, Anais.. Philadelphia: SIAM, 2013. Disponível em: https://doi.org/10.1137/1.9781611972832.32. Acesso em: 01 nov. 2024.
APA
Acharya, A., Hruschka, E. R., Ghosh, J., Sarwar, B., & Ruvini, J. -D. (2013). Probabilistic combination of classifier and cluster ensembles for non-transductive learning. In Proceedings. Philadelphia: SIAM. doi:10.1137/1.9781611972832.32
NLM
Acharya A, Hruschka ER, Ghosh J, Sarwar B, Ruvini J-D. Probabilistic combination of classifier and cluster ensembles for non-transductive learning [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1137/1.9781611972832.32
Vancouver
Acharya A, Hruschka ER, Ghosh J, Sarwar B, Ruvini J-D. Probabilistic combination of classifier and cluster ensembles for non-transductive learning [Internet]. Proceedings. 2013 ;[citado 2024 nov. 01 ] Available from: https://doi.org/10.1137/1.9781611972832.32

USP Schools

ReP

Filtros

Authors

USP affiliated authors

Date published

Subjects

Source title

Publisher

Conference titles

Countries/Territories

Indexado em

Normalized affiliation

Non normalized affiliation