César Lincoln C. Mattos

Orcid: 0000-0002-2404-3625

According to our database1, César Lincoln C. Mattos authored at least 37 papers between 2013 and 2023.

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

Timeline

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Thin and Deep Gaussian Processes.
CoRR, 2023

Minimal Learning Machine for Multi-Label Learning.
CoRR, 2023

CVEjoin: An Information Security Vulnerability and Threat Intelligence Dataset.
Proceedings of the Advanced Information Networking and Applications, 2023

A Vulnerability Risk Assessment Methodology Using Active Learning.
Proceedings of the Advanced Information Networking and Applications, 2023

2022
Bayesian Multilateration.
IEEE Signal Process. Lett., 2022

Self-tuning portfolio-based Bayesian optimization.
Expert Syst. Appl., 2022

The role of bug report evolution in reliable fixing estimation.
Empir. Softw. Eng., 2022

A Text Classification Methodology to Assist a Large Technical Support System.
IEEE Access, 2022

Bayesian Analysis of Bug-Fixing Time using Report Data.
Proceedings of the ESEM '22: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, Helsinki, Finland, September 19, 2022

2021
A novel fuzzy ARTMAP with area of influence.
Neurocomputing, 2021

A Practical Guide to Support Predictive Tasks in Data Science.
Proceedings of the 23rd International Conference on Enterprise Information Systems, 2021

FakeWhastApp.BR: NLP and Machine Learning Techniques for Misinformation Detection in Brazilian Portuguese WhatsApp Messages.
Proceedings of the 23rd International Conference on Enterprise Information Systems, 2021

Learning GPLVM with arbitrary kernels using the unscented transformation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
LS-SVR as a Bayesian RBF Network.
IEEE Trans. Neural Networks Learn. Syst., 2020

A sparse linear regression model for incomplete datasets.
Pattern Anal. Appl., 2020

Detectando Doença de Parkinson - Uma Comparação de Modelos de Aprendizagem de Máquina com Redução de Dimensionalidade Diferencialmente Privada.
Proceedings of the 35th Brazilian Symposium on Databases, 2020

An Optimized Approach to Huntington's Disease Detecting via Audio Signals Processing with Dimensionality Reduction.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Anomaly Detection in Trajectory Data with Normalizing Flows.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

A New Methodology for Classifying QRS Morphology in ECG Signals.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

On the Use of Cultural Enhancement Strategies to Improve the NEAT Algorithm.
Proceedings of the IEEE Congress on Evolutionary Computation, 2020

2019
A stochastic variational framework for Recurrent Gaussian Processes models.
Neural Networks, 2019

Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection.
CoRR, 2019

Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels.
CoRR, 2019

OnMLM: An Online Formulation for the Minimal Learning Machine.
Proceedings of the Advances in Computational Intelligence, 2019

No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Sparse minimal learning machine using a diversity measure minimization.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Evaluation of Data Based Normal Behavior Models for Fault Detection in Wind Turbines.
Proceedings of the 8th Brazilian Conference on Intelligent Systems, 2019

Audio Plugin Recommendation Systems for Music Production.
Proceedings of the 8th Brazilian Conference on Intelligent Systems, 2019

2017
Metaheuristic optimization for automatic clustering of customer-oriented supply chain data.
Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017

Randomized Neural Networks for Recursive System Identification in the Presence of Outliers: A Performance Comparison.
Proceedings of the Advances in Computational Intelligence, 2017

2016
Recurrent Gaussian Processes.
Proceedings of the 4th International Conference on Learning Representations, 2016

2015
Performance Evaluation of Least Squares SVR in Robust Dynamical System Identification.
Proceedings of the Advances in Computational Intelligence, 2015

An Empirical Evaluation of Robust Gaussian Process Models for System Identification.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2015, 2015

2014
An improved hybrid particle swarm optimization algorithm applied to economic modeling of radio resource allocation.
Electron. Commer. Res., 2014

A Novel Recursive Kernel-Based Algorithm for Robust Pattern Classification.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2014, 2014

Improved Adaline Networks for Robust Pattern Classification.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

2013
ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks.
Neural Comput. Appl., 2013


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