Bruno M. Nogueira

According to our database1, Bruno M. Nogueira authored at least 14 papers between 2006 and 2020.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.



In proceedings 
PhD thesis 


Online presence:



TextCSN: a semi-supervised approach for text clustering using pairwise constraints and convolutional siamese network.
Proceedings of the SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30, 2020

Machine Learning for Suicidal Ideation Identification on Twitter for the Portuguese Language.
Proceedings of the Intelligent Systems - 9th Brazilian Conference, 2020

Integrating distance metric learning and cluster-level constraints in semi-supervised clustering.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Constrained Hierarchical Clustering for News Events.
Proceedings of the 21st International Database Engineering & Applications Symposium, 2017

Learning a Fast Bipartite Ranker for Text Documents Using Lexicographical Rankers and ROC Curves.
Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition, 2017

A multidimensional data model for the analysis of learning management systems under different perspectives.
Proceedings of the 2016 IEEE Frontiers in Education Conference, 2016

Agrupamento hierárquico semissupervisionado ativo baseado em confiança e sua aplicação para extração de hierarquias de tópicos a partir de coleções de documentos.
PhD thesis, 2013

Comparing relational and non-relational algorithms for clustering propositional data.
Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013

Hierarchical confidence-based active clustering.
Proceedings of the ACM Symposium on Applied Computing, 2012

HCAC: Semi-supervised Hierarchical Clustering Using Confidence-Based Active Learning.
Proceedings of the Discovery Science - 15th International Conference, 2012

Comparison of Classifiers Efficiency on Missing Values Recovering: Application in a Marketing Database with Massive Missing Data.
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2007

Techniques for Missing Value Recovering in Imbalanced Databases: Application in a Marketing Database with Massive Missing Data.
Proceedings of the IEEE International Conference on Systems, 2006

SOPHIANN: A Tool for Extraction Knowledge Rules from ANN Previously Trained A Case Study.
Proceedings of the Eighteenth International Conference on Software Engineering & Knowledge Engineering (SEKE'2006), 2006

Techniques for Training Sets Selection in the Representation of a Thermosiphon System Via ANN.
Proceedings of the International Joint Conference on Neural Networks, 2006