Paulo J. L. Adeodato

发表

A Decision Support System Based on Data Mining for Pediatric Cardiology Diagnosis

Paulo J. L. Adeodato, Tarcísio B. Gurgel, Sandra Mattos, 2009, DMIN.

Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining pdf

Paulo J. L. Adeodato, Fábio C. Pereira, Rosalvo F. Oliveira Neto, 2017, ArXiv.

Variable Transformation for Granularity Change in Hierarchical Databases in Actual Data Mining Solutions

Paulo J. L. Adeodato, P. Adeodato, 2015, IDEAL.

Data transformations and seasonality adjustments improve forecasts of MLP ensembles

Paulo J. L. Adeodato, Domingos S. P. Salazar, Adrian L. Arnaud, 2012, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems.

Improving reinforcement learning algorithms by the use of data mining techniques for feature and action selection

Paulo J. L. Adeodato, Paulo Gonçalves, Davi C. L. Vieira, 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

Integration and knowledge reuse environment for producing award winning solutions for binary decision data mining problems

Silvio Romero de Lemos Meira, Paulo J. L. Adeodato, Rodrigo C. L. V. Cunha, 2009, 2009 IEEE International Conference on Information Reuse & Integration.

A systematic solution for the NN3 Forecasting Competition problem based on an ensemble of MLP neural networks

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2008, 2008 19th International Conference on Pattern Recognition.

Domain Driven Data Mining for Unavailability Estimation of Electrical Power Grids

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2010, IEA/AIE.

Learnability in sequential RAM-based neural networks

Paulo J. L. Adeodato, Marcílio Carlos Pereira de Souto, 1998, Proceedings 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209).

An Approach for Learning from Small and Unbalanced Data Sets Using Gaussian Noise During Artificial Neural Network Training

Paulo J. L. Adeodato, Icamaan Viegas, 2010, DMIN.

DECISION SUPPORT SYSTEM FOR PEDIATRIC CARDIOLOGY DIAGNOSIS

Paulo J. L. Adeodato, Tarcisio B. Gurgel, Sandra S. Mattos, 2009 .

ARENA—Augmented Reality to Enhanced Experimentation in Smart Warehouses

André Schneider de Oliveira, Paulo J. L. Adeodato, Luis Piardi, 2019, Sensors.

Hybrid Approach for Time Series Prediction using Neural Networks and Simulated Annealing

Germano C. Vasconcelos, Paulo J. L. Adeodato, Rosalvo F. Oliveira Neto, 2016 .

Stability analysis of pRAM reinforcement learning

John G. Taylor, Paulo J. L. Adeodato, 1997, Proceedings 4th Brazilian Symposium on Neural Networks.

Polynomial approximation RAM neuron capable of handling true continuous input variables

Paulo J. L. Adeodato, Rosalvo F. Oliveira Neto, P. J. L. Adeodato, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Data Mining Solution for Assessing Brazilian Secondary School Quality Based on ENEM and Census Data

Paulo J. L. Adeodato, 2016 .

CoMoVi: a Framework for Data Transformation in Credit Behavioral Scoring Applications Using Model Driven Architecture

Ana Carolina Salgado, Paulo J. L. Adeodato, Rosalvo F. O. Neto, 2014, SEKE.

A framework for data transformation in Credit Behavioral Scoring applications based on Model Driven Development

Ana Carolina Salgado, Paulo J. L. Adeodato, Rosalvo F. O. Neto, 2017, Expert Syst. Appl..

New Frontiers in Applied Data Mining, PAKDD 2009 International Workshops, Bangkok, Thailand, April 27-30, 2009. Revised Selected Papers

Nitesh V. Chawla, Graham J. Williams, Paulo J. L. Adeodato, 2010, PAKDD Workshops.

PCA and Gaussian noise in MLP neural network training improve generalization in problems with small and unbalanced data sets

Paulo J. L. Adeodato, Icamaan B. Viegas da Silva, P. Adeodato, 2011, The 2011 International Joint Conference on Neural Networks.

Predicting software defects: A cost-sensitive approach

Paulo J. L. Adeodato, Adriano Lorena Inácio de Oliveira, Miguel E. R. Bezerra, 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

Sequential RAM-based Neural Networks: Learnability, Generalisation, Knowledge Extraction, and Grammatical Inference

Paulo J. L. Adeodato, Marcílio Carlos Pereira de Souto, Teresa Bernarda Ludermir, 1999, Int. J. Neural Syst..

A Temporal Difference GNG-Based Algorithm That Can Learn to Control in Reinforcement Learning Environments

Paulo J. L. Adeodato, Davi C. de L. Vieira, Paulo M. Goncalves Junior, 2013, 2013 12th International Conference on Machine Learning and Applications.

pRAM n-tuple Classifier - a new architecture of probabilistic RAM neurons for classification problems

Paulo J. L. Adeodato, Rosalvo F. O. Neto, P. J. L. Adeodato, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Autoassociative Memory with high Storage Capacity

John G. Taylor, Paulo J. L. Adeodato, 1996, ICANN.

Enhancing RBF-DDA Algorithm's Robustness: Neural Networks Applied to Prediction of Fault-Prone Software Modules

Silvio Romero de Lemos Meira, Paulo J. L. Adeodato, Adriano Lorena Inácio de Oliveira, 2008, IFIP AI.

A data mining approach for preventing undergraduate students retention

Paulo J. L. Adeodato, Hadautho Roberto Barros da Silva, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

A New Evolutionary Approach for Time Series Forecasting

Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato, 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

Continuous variables segmentation and reordering for optimal performance on binary classification tasks

Paulo J. L. Adeodato, Domingos S. P. Salazar, Lucas S. Gallindo, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

A data mining approach to solve the goal scoring problem

Paulo J. L. Adeodato, Ing Ren Tsang, Arthur Carvalho, 2009, 2009 International Joint Conference on Neural Networks.

A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato, 2008, Neural Processing Letters.

The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge

Germano C. Vasconcelos, Paulo J. L. Adeodato, Tarcísio B. Gurgel, 2009, 2009 International Joint Conference on Neural Networks.

On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classification pdf

Paulo J. L. Adeodato, P. J. L. Adeodato, Sílvio B. Melo, 2016, ArXiv.

DECISION SUPPORT SYSTEM FOR PREVENTING NO-SHOW TO MEDICALAPPOINTMENTS

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2008 .

Evaluation of the use of computational intelligence techniques in medical claim processes of a health insurance company

Paulo J. L. Adeodato, Flávio H. D. Araújo, Erico Leão, 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

Estudo Comparativo entre Proposicionalização e Mineração de Dados Multidimensional sobre um Banco de Dados Relacional

Ana Carolina Salgado, Paulo J. L. Adeodato, Murilo Boratto, 2012, SBBD.

Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining

Nitesh V. Chawla, Graham J. Williams, Paulo J. L. Adeodato, 2009 .

DATA MINING APPLIED TO THE PROCESSES CELERITY OF PERNAMBUCO'SSTATE COURT OF ACCOUNTS

Francisco de A. T. de Carvalho, Paulo J. L. Adeodato, Adrian L. Arnaud, 2008 .

Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series

Paulo J. L. Adeodato, Domingos S. P. Salazar, Adrian L. Arnaud, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2004, ICPR 2004.

A new evolutionary method for time series forecasting

Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato, 2005, GECCO '05.

Fat Tailed Distribution of Neural Networks Forecasting

Paulo J. L. Adeodato, Domingos S. P. Salazar, Adrian L. Arnaud, 2010, DMIN.

A New Hybrid Method for Time Series Forecasting

Germano C. Vasconcelos, Tiago A. E. Ferreira, Paulo J. L. Adeodato, 2016 .

Equivalência entre a Área sob a Curva Kolmogorov-Smirnov e o Índice de Gini na Avaliação de Desempenho de Decisões Binárias

Paulo J. L. Adeodato, Sílvio B. Melo, Paulo J. L. Adeodato, 2016, SBBD.

MLP ensembles improve long term prediction accuracy over single networks

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2011 .

Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2004, ICPR.

Knowledge Reuse in Data Mining Projects and Its Practical Applications

Silvio Romero de Lemos Meira, Paulo J. L. Adeodato, Rodrigo C. L. V. Cunha, 2009, ICEIS.

The Power of Sampling and Stacking for the PAKDD-2007 Cross-Selling Problem

Germano C. Vasconcelos, Paulo J. L. Adeodato, Adrian L. Arnaud, 2010, Strategic Advancements in Utilizing Data Mining and Warehousing Technologies.

A hybrid intelligent system approach for improving the prediction of real world time series

Tiago Alessandro Espínola Ferreira, Germano C. Vasconcelos, Paulo J. L. Adeodato, 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

A Temporal Difference GNG-Based Approach for the State Space Quantization in Reinforcement Learning Environments

Paulo J. L. Adeodato, Paulo Mauricio Gonçalves, Davi Carnauba de Lima Vieira, 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.