G. Barreto

G. Barreto, Zhanxing Zhu, F. Corona, 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

Novel Algorithms for Nonlinear Channel Equalization Using Neural Vector Quantization

G. Barreto, J. Mota, G. M. Souza, 2005 .

Mechanical properties prediction in rebar using kernel-based regression models

G. Barreto, E. P. de Moura, R. H. F. Murta, 2022, Ironmaking & Steelmaking.

A Sparse Online Approach for Streaming Data Classification via Prototype-Based Kernel Models

David N. Coelho, G. Barreto, D. N. Coelho, 2022, Neural Processing Letters.

On the Performance of Neural Networks for Face Recognition: Linear or Nonlinear Classifiers?

G. Barreto, Rafael O. Lima, 2016 .

Classificação Local utilizando Least Square Support Vector Machine (LSSVM)

Rômulo Bandeira Pimentel Drumond, G. Barreto, R. Albuquerque, 2020, Anais do 14. Congresso Brasileiro de Inteligência Computacional.

Randomized Pattern Classifiers for Epileptic Seizure Detection : A Critical Assessment

G. Barreto, N. R. Silva, Júlio P. Silva Júnior, 2017 .

An Outlier-Robust Growing Local Model Network for Recursive System Identification

J. A. Bessa, Ajalmar R. Rocha-Neto, G. Barreto, 2022, Neural Processing Letters.

Pattern classification based on regional models

Rômulo Bandeira Pimentel Drumond, A. Souza, G. Barreto, 2022, Appl. Soft Comput..

A novel weight pruning method for MLP classifiers based on the MAXCORE principle

Cláudio M. S. Medeiros, G. Barreto, 2011, Neural Computing and Applications.

Novel approaches for parameter estimation of local linear models for dynamical system identification

G. Barreto, L. G. Souza, 2015, Applied Intelligence.

A unifying methodology for the evaluation of neural network models on novelty detection tasks

G. Barreto, R. A. Frota, 2013, Pattern Analysis and Applications.

Local Linear NARX Models Based On The Self-Organizing Map

Guilherme A. Barreto, Luís Gustavo M. Souza, G. Barreto, 2016 .

Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

Guilherme De A. Barreto, José Daniel A. Santos, Cláudio M. S. Medeiros, 2014, 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES).

Robust classification with reject option using the self-organizing map

Jaime S. Cardoso, Ajalmar R. da Rocha Neto, Guilherme De A. Barreto, 2015, Neural Computing and Applications.

Neural Computing and Applications Robust Classification with Reject Option Using the Self-Organizing Map

Jaime S. Cardoso, Jaime S Cardoso, G. Barreto, 2014 .

ARTIE and MUSCLE models: building ensemble classifiers from fuzzy ART and SOM networks

Guilherme De A. Barreto, César Lincoln C. Mattos, C. Mattos, 2011, Neural Computing and Applications.

On Self-Organizing Feature Map (SOFM) Formation by Direct Optimization Through a Genetic Algorithm

Guilherme De A. Barreto, André L. V. Coelho, José Everardo Bessa Maia, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison

Guilherme De A. Barreto, Cláudio M. S. Medeiros, Charles C. Cavalcante, 2019, WSOM+.

Evaluation of Goat Leather Quality Based on Computational Vision Techniques

Guilherme A. Barreto, Victor Hugo C. de Albuquerque, Pedro Pedrosa Rebouças Filho, 2020, Circuits Syst. Signal Process..

On the Design of Robust Linear Pattern Classifiers Based on $$M$$M-Estimators

Guilherme De A. Barreto, Ana Luiza B. P. Barros, G. Barreto, 2015, Neural Processing Letters.

Long-term time series prediction with the NARX network: An empirical evaluation

Guilherme De A. Barreto, José Maria P. Menezes, G. Barreto, 2008, Neurocomputing.

A New Look at Nonlinear Time Series Prediction with NARX Recurrent Neural Network

Guilherme De A. Barreto, José Maria P. Menezes, G. Barreto, 2006, 2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06).

On recurrent neural networks for auto-similar traffic prediction: A performance evaluation

J.M.P. Menezes, G.A. Barreto, G. Barreto, 2006, 2006 International Telecommunications Symposium.

Adaptive filtering with the self-organizing map: A performance comparison

Guilherme De A. Barreto, Luís Gustavo M. Souza, G. Barreto, 2006, Neural Networks.

Predictive Modeling and Planning of Robot Trajectories Using the Self-Organizing Map

Aluizio F. R. Araújo, Guilherme De A. Barreto, G. Barreto, 2004, IEA/AIE.

A distributed robotic control system based on a temporal self-organizing neural network

Helge J. Ritter, Aluizio F. R. Araújo, Guilherme De A. Barreto, 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case

Aluizio F. R. Araújo, Guilherme De A. Barreto, Stefan C. Kremer, 2003, Neural Computation.

A Self-Organizing Context-Based Approach to the Tracking of Multiple Robot Trajectories

Aluizio F. R. Araújo, Guilherme De A. Barreto, G. Barreto, 2002, Applied Intelligence.

Storage and recall of complex temporal sequences through a contextually guided self-organizing neural network

Aluizio F. R. Araújo, Guilherme De A. Barreto, G. Barreto, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Novelty Detection in Time Series Using Self-Organizing Neural Networks: A Comprehensive Evaluation

Leonardo Aguayo, Guilherme De A. Barreto, G. Barreto, 2017, Neural Processing Letters.

Time Series Clustering For Novelty Detection: An Empirical Approach

Leonardo Aguayo, Guilherme A. Barreto, G. Barreto, 2016 .

Time Series Clustering for Anomaly Detection Using Competitive Neural Networks

Leonardo Aguayo, Guilherme De A. Barreto, G. Barreto, 2009, WSOM.

Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks

Leonardo Aguayo, Guilherme A. Barreto, G. Barreto, 2007 .

Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators

Helge J. Ritter, Aluizio F. R. Araújo, Guilherme De A. Barreto, 2003, J. Intell. Robotic Syst..

On building local models for inverse system identification with vector quantization algorithms

Guilherme De A. Barreto, Luís Gustavo M. Souza, G. Barreto, 2010, Neurocomputing.

Context in temporal sequence processing: a self-organizing approach and its application to robotics

Aluizio F. R. Araújo, Guilherme De A. Barreto, G. Barreto, 2002, IEEE Trans. Neural Networks.

Unsupervised context-based learning of multiple temporal sequences

Aluizio F. R. Araujo, Guilherme de A. Berreto, G. Barreto, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Revisiting the modeling of wind turbine power curves using neural networks and fuzzy models: an application-oriented evaluation

Guilherme A. Barreto, Igor S. Brasil, Luis Gustavo M. Souza, 2021, Energy Systems.

Detection of short circuit faults in 3-phase converter-fed induction motors using kernel SOMs

Guilherme De A. Barreto, Cláudio M. S. Medeiros, David N. Coelho, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Estimating the Number of Hidden Neurons of the MLP Using Singular Value Decomposition and Principal Components Analysis: A Novel Approach

Guilherme De A. Barreto, José Daniel A. Santos, Cláudio M. S. Medeiros, 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.

Metaheuristic optimization for automatic clustering of customer-oriented supply chain data

Bernd Hellingrath, Guilherme De A. Barreto, Dennis Horstkemper, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

A Robust Extreme Learning Machine for pattern classification with outliers

Guilherme De A. Barreto, Ana Luiza B. P. Barros, G. Barreto, 2016, Neurocomputing.

Kinesthetic teaching of visuomotor coordination for pointing by the humanoid robot iCub

Jochen J. Steil, Guilherme De A. Barreto, Ananda Freire, 2013, Neurocomputing.

Novelty Detection in Time Series Through Self-Organizing Networks: An Empirical Evaluation of Two Different Paradigms

Leonardo Aguayo, Guilherme De A. Barreto, G. Barreto, 2008, 2008 10th Brazilian Symposium on Neural Networks.

On robust randomized neural networks for regression: a comprehensive review and evaluation

Ananda L. Freire, Ajalmar R. Rocha-Neto, Guilherme A. Barreto, 2020, Neural Computing and Applications.

Improving the Classification Performance of Optimal Linear Associative Memory in the Presence of Outliers

Guilherme De A. Barreto, Ana Luiza B. P. Barros, G. Barreto, 2013, IWANN.

Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study

Guilherme A. Barreto, Ananda L. Freire, Marcus Veloso, 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

A Speech Recognition System for Embedded Applications Using the SOM and TS-SOM Networks

Guilherme A. Barreto, Antonio T. Varela, Amauri H. Souza Júnior, 2011 .

Visual object tracking by an evolutionary self-organizing neural network

Guilherme De A. Barreto, André L. V. Coelho, José Everardo Bessa Maia, 2011, J. Intell. Fuzzy Syst..

Latent Autoregressive Gaussian Processes Models for Robust System Identification

Neil D. Lawrence, Guilherme A. Barreto, Andreas Damianou, 2016 .

A General Framework for Optimal Tuning of PID-like Controllers for Minimum Jerk Robotic Trajectories

Guilherme A. Barreto, Phelipe W. Oliveira, George A. P. Thé, 2020, Journal of Intelligent & Robotic Systems.

Time Series Prediction with the Self-Organizing Map: A Review

Guilherme De A. Barreto, G. Barreto, 2007, Perspectives of Neural-Symbolic Integration.

Minimal Learning Machine: A New Distance-Based Method for Supervised Learning

Amaury Lendasse, Olli Simula, Francesco Corona, 2013, IWANN.

A new model selection approach for the ELM network using metaheuristic optimization

Guilherme De A. Barreto, Ananda Freire, G. Barreto, 2014, ESANN.

Gaussian processes with logistic mean function for modeling wind turbine power curves

Guilherme A. Barreto, César Lincoln C. Mattos, Gustavo C.M. Virgolino, 2020 .

Identification and control of dynamical systems using the self-organizing map

Aluizio F. R. Araújo, Guilherme De A. Barreto, G. Barreto, 2004, IEEE Transactions on Neural Networks.

Recurrent Gaussian Processes

Andreas C. Damianou, Neil D. Lawrence, A. Damianou, 2015, ICLR.

A stochastic variational framework for Recurrent Gaussian Processes models

Guilherme De A. Barreto, César Lincoln C. Mattos, C. Mattos, 2019, Neural Networks.

Deep recurrent Gaussian processes for outlier-robust system identification

Neil D. Lawrence, Guilherme A. Barreto, Andreas Damianou, 2017 .

Pruning the Multilayer Perceptron through the Correlation of Backpropagated Errors

Guilherme De A. Barreto, Cláudio M. S. Medeiros, Cláudio M. S. Medeiros, 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

An Efficient Method for Pruning the Multilayer Perceptron Based on the Correlation of Errors

Guilherme De A. Barreto, Cláudio M. S. Medeiros, Cláudio M. S. Medeiros, 2007, ICANN.

A Novel Kalman Filter Formulation for Improving Tracking Performance of the Extended Kernel RLS

José S. Costa Filho, Guilherme A. Barreto, Tiago dos Santos Façanha, 2020, Circuits Syst. Signal Process..

Minimal Learning Machine: A novel supervised distance-based approach for regression and classification

Amaury Lendasse, Francesco Corona, Guilherme De A. Barreto, 2015, Neurocomputing.

Temporal Classification of Turbofan Engine Health using Elman Recurrent Network

Guilherme A. Barreto, Navar de Medeiros Mendonça e Nascimento, Cairo Nascimento Júnior, 2020, Anais do 14. Congresso Brasileiro de Inteligência Computacional.

An improved hybrid particle swarm optimization algorithm applied to economic modeling of radio resource allocation

Guilherme De A. Barreto, Francisco Rodrigo Porto Cavalcanti, César Lincoln C. Mattos, 2014, Electron. Commer. Res..

Classification with Reject Option Using the Self-Organizing Map

Jaime S. Cardoso, Ajalmar R. da Rocha Neto, Guilherme De A. Barreto, 2014, ICANN.

SOM4R: a Middleware for Robotic Applications Based on the Resource-Oriented Architecture

José S. Costa Filho, Guilherme De A. Barreto, José Tarcisio Costa Filho, 2017, J. Intell. Robotic Syst..

Novel approaches for online playout delay prediction in VoIP applications using time series models

Guilherme De A. Barreto, José B. Aragão, G. Barreto, 2010, Comput. Electr. Eng..

Improved Adaline Networks for Robust Pattern Classification

Guilherme De A. Barreto, José Daniel A. Santos, César Lincoln C. Mattos, 2014, ICANN.

Application and Evaluation of Multi-criteria Clustering Algorithms for Customer-Oriented Supply Chain Segmentation

Bernd Hellingrath, Sebastian Terlunen, Guilherme Barreto, 2015 .

A regularized estimation framework for online sparse LSSVR models

Guilherme De A. Barreto, José Daniel A. Santos, G. Barreto, 2017, Neurocomputing.

Novel approaches for parameter estimation of local linear models for dynamical system identification

Guilherme De A. Barreto, Luís Gustavo M. Souza, G. Barreto, 2015, Applied Intelligence.

Performance Evaluation of Least Squares SVR in Robust Dynamical System Identification

Guilherme De A. Barreto, José Daniel A. Santos, César Lincoln C. Mattos, 2015, IWANN.

Regional models: A new approach for nonlinear system identification via clustering of the self-organizing map

Francesco Corona, Guilherme De A. Barreto, Amauri H. Souza Júnior, 2015, Neurocomputing.

A GA-Based Approach for Building Regularized Sparse Polynomial Models for Wind Turbine Power Curves

Guilherme A. Barreto, Haroldo C. Maya, G. Barreto, 2018, Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018).

Forward Stagewise Regression on Incomplete Datasets

Guilherme De A. Barreto, João P. P. Gomes, Amauri H. Souza Júnior, 2017, IWANN.

Evolving a Self-Organizing Feature Map for Visual Object Tracking

Guilherme De A. Barreto, André L. V. Coelho, José Everardo Bessa Maia, 2011, WSOM.

Face recognition independent of facial expression through SOM-based classifiers

G.A. Barreto, I.Q. Monteiro, S.D. Queiroz, 2006, 2006 International Telecommunications Symposium.

Multiple Local ARX Modeling for System Identification Using the Self-Organizing Map

Guilherme A. Barreto, G. Barreto, L. G. Souza, 2008 .

Opposite Maps: Vector Quantization Algorithms for Building Reduced-Set SVM and LSSVM Classifiers

G. Barreto, A. Neto, 2013, Neural Processing Letters.

A Novel Recursive Solution to LS-SVR for Robust Identification of Dynamical Systems

Guilherme De A. Barreto, José Daniel A. Santos, G. Barreto, 2015, IDEAL.