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1
Particle competition for unbalanced community detection in complex networks
Particle competition for unbalanced community detection in complex networks
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Particle competition for unbalanced community detection in complex networks

Luan Vinicius Carvalho Martins Zhao Liang; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 322-336, 2020

Cham Springer 2020

Localização: FFCLRP - Fac. Fil. Ciên. Let. de R. Preto    (pcd 3008291 Acervo Digital ) e outros locais(Acessar)

2
2CS correlation-guided split candidate selection in Hoeffding tree regressors
2CS correlation-guided split candidate selection in Hoeffding tree regressors
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

2CS correlation-guided split candidate selection in Hoeffding tree regressors

Saulo Martiello Mastelini André Carlos Ponce de Leon Ferreira de Carvalho; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 337-351, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008273 )(Acessar)

3
A fuzzy approach for classification and novelty detection in data streams under intermediate latency
A fuzzy approach for classification and novelty detection in data streams under intermediate latency
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

A fuzzy approach for classification and novelty detection in data streams under intermediate latency

André Luis Cristiani Tiago Pinho da Silva; Heloisa de Arruda Camargo; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 171-186, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008365 )(Acessar)

4
Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network
Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Predicting the evolution of COVID-19 cases and deaths through a correlations-based temporal network

Tiago Santos Colliri Alexandre Cláudio Botazzo Delbem; Zhao Liang; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 397-411, 2020

Cham Springer 2020

Localização: FFCLRP - Fac. Fil. Ciên. Let. de R. Preto    (pcd 3008293 Acervo Digital ) e outros locais(Acessar)

5
Identifying fine-grained opinion and classifying polarity on coronavirus pandemic
Identifying fine-grained opinion and classifying polarity on coronavirus pandemic
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Identifying fine-grained opinion and classifying polarity on coronavirus pandemic

Francielle Alves Vargas Rodolfo Sanches Saraiva dos Santos; Pedro Regattieri Rocha; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12319, p. 511-520, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008368 )(Acessar)

6
Robust ranking of brazilian Supreme Court decisions
Robust ranking of brazilian Supreme Court decisions
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Robust ranking of brazilian Supreme Court decisions

Jackson José de Souza Marcelo Finger; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande, Brasil)

Proceedings Cham: Springer, 2020

Cham Springer 2020

Localização: IME - Inst. Matemática e Estatística    (PROD-3013511 )(Acessar)

7
Data streams are time series challenging assumptions
Data streams are time series challenging assumptions
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Data streams are time series challenging assumptions

Jesse Read Ricardo A Rios; Tatiane Nogueira; Rodrigo Fernandes de Mello; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 529-543, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008278 )(Acessar)

8
Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection
Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Quantifying temporal novelty in social networks using time-varying graphs and concept drift detection

Victor M. G. dos Santos Rodrigo Fernandes de Mello; Tatiane Nogueira; Ricardo A Rios; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 650-664, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008282 )(Acessar)

9
Simulating complexity measures on imbalanced datasets
Simulating complexity measures on imbalanced datasets
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

Simulating complexity measures on imbalanced datasets

Victor Hugo Barella Luís Paulo Faina Garcia; André Carlos Ponce de Leon Ferreira de Carvalho; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande)

Lecture Notes in Artificial Intelligence Cham : Springer v. 12320, p. 498-512, 2020

Cham Springer 2020

Localização: ICMC - Inst. Ciên. Mat. Computação    (PROD-3008277 )(Acessar)

10
On the performance of planning through backpropagation
On the performance of planning through backpropagation
Material Type:
Artigo de Congresso 		Adicionar ao Meu Espaço

On the performance of planning through backpropagation

Renato Scaroni Thiago Pereira Bueno; Leliane Nunes de Barros; Denis Deratani Mauá; Brazilian Conference on Intelligent Systems - BRACIS (9. 2020 Rio Grande, Brasil)

Proceedings Cham: Springer, 2020

Cham Springer 2020

Localização: IME - Inst. Matemática e Estatística    (PROD-3013498 )(Acessar)

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