| A machine-learning methodology using domain-knowledge constraints for well-data integration and well-production prediction J Guevara, B Zadrozny, A Buoro, L Lu, J Tolle, JW Limbeck, D Hohl SPE Reservoir Evaluation & Engineering 22 (04), 1,185-1,200, 2019 | 18 | 2019 |
| Positive Definite Kernel Functions on Fuzzy Sets J Guevara, R Hirata, S Canu Fuzzy Systems (FUZZ), 2014 IEEE International Conference on,Beijing-China, 2014 | 15 | 2014 |
| Kernel functions in Takagi-Sugeno-Kang fuzzy system with nonsingleton fuzzy input J Guevara, R Hirata, S Canu Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, Hyderabad-India …, 2013 | 14 | 2013 |
| Support Measure Data Description for group anomaly detection J Guevara, S Canu, R Hirata ODDx3 Workshop on Outlier Definition, Detection, and Description at the 21st …, 2015 | 13 | 2015 |
| A data-driven workflow for predicting horizontal well production using vertical well logs J Guevara, M Kormaksson, B Zadrozny, L Lu, J Tolle, T Croft, M Wu, ... arXiv preprint arXiv:1705.06556, 2017 | 12 | 2017 |
| Cross product kernels for fuzzy set similarity J Guevara, R Hirata, S Canu 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6, 2017 | 9 | 2017 |
| A Hybrid Data-Driven and Knowledge-Driven Methodology for Estimating the Effect of Completion Parameters on the Cumulative Production of Horizontal Wells J Guevara, B Zadrozny, A Buoro, L Lu, J Tolle, J Limbeck, M Wu, D Hohl SPE Annual Technical Conference and Exhibition, 2018 | 8 | 2018 |
| Fuzzy Set Similarity using a Distance-Based Kernel on Fuzzy Sets J Guevara, R Hirata, S Canu Handbook of Fuzzy Sets Comparison Theory, Algorithms and Applications 6, 103-120, 2016 | 5 | 2016 |
| Support measure data description J Guevara, R Canu Support measure data description. submitted to pattern analysis and machine …, 2014 | 4* | 2014 |
| Supervised machine learning with kernel embeddings of fuzzy sets and probability measures JG Diaz IME USP, 2016 | 3 | 2016 |
| Supervised machine learning with kernel embeddings of fuzzy sets and probability measures J Guevara | 3 | 2016 |
| Modelos de aprendizado supervisionado usando métodos kernel, conjuntos fuzzy e medidas de probabilidade J Guevara Instituto de Matemática e Estatística, Universidade de Sao Paulo, Brazil, 2015 | 3 | 2015 |
| Propagation of well-log data imputation uncertainties towards the interpolated 3D-petrophysical map using epistemic kernels and kriging JLG Diaz, B Zadrozny 82nd EAGE Annual Conference & Exhibition 2021 (1), 1-5, 2021 | 2 | 2021 |
| A modular framework for extreme weather generation B Zadrozny, CD Watson, D Szwarcman, D Civitarese, D Oliveira, ... arXiv preprint arXiv:2102.04534, 2021 | 2 | 2021 |
| System, method and computer program product for dispatching of medical emergencies and wait time control CH Cardonha, J Guevara, BN Goncalves, MA Vasconcelos US Patent App. 15/279,632, 2018 | 2 | 2018 |
| Support Measure Data Description J Guevara, S Canu, R Hirata Jr | 2 | 2014 |
| Controlling Weather Field Synthesis Using Variational Autoencoders DAB Oliveira, JG Diaz, B Zadrozny, CD Watson, XX Zhu IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | 1 | 2022 |
| Fuzzy-System Kernel Machines: A Kernel Method Based on the Connections Between Fuzzy Inference Systems and Kernel Machines J Guevara, JM Mendel, R Hirata IEEE Transactions on Fuzzy Systems 30 (10), 4447-4459, 2022 | 1 | 2022 |
| A comparative study of stochastic and deep generative models for multisite precipitation synthesis J Guevara, D Borges, C Watson, B Zadrozny arXiv preprint arXiv:2107.08074, 2021 | 1 | 2021 |
| Connections Between Fuzzy Inference Systems and Kernel Machines J Guevara, JM Mendel, R Hirata 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-8, 2020 | 1 | 2020 |
Bianca ZadroznyIBM Research BrasilVerified email at br.ibm.com
