| Weak supervision and other non-standard classification problems: a taxonomy J Hernández-González, I Inza, JA Lozano Pattern Recognition Letters 69, 49-55, 2016 | 79 | 2016 |
| Learning bayesian network classifiers from label proportions J Hernández-González, I Inza, JA Lozano Pattern Recognition 46 (12), 3425-3440, 2013 | 55 | 2013 |
| Fitting the data from embryo implantation prediction: Learning from label proportions J Hernández-González, I Inza, L Crisol-Ortíz, MA Guembe, MJ Iñarra, ... Statistical methods in medical research 27 (4), 1056-1066, 2018 | 15 | 2018 |
| Learning to classify software defects from crowds: a novel approach J Hernández-González, D Rodriguez, I Inza, R Harrison, JA Lozano Applied Soft Computing 62, 579-591, 2018 | 14 | 2018 |
| Multidimensional learning from crowds: Usefulness and application of expertise detection J Hernández‐González, I Inza, JA Lozano International Journal of Intelligent Systems 30 (3), 326-354, 2015 | 9 | 2015 |
| Learning from proportions of positive and unlabeled examples J Hernández‐González, I Inza, JA Lozano International Journal of Intelligent Systems 32 (2), 109-133, 2017 | 8 | 2017 |
| Learning naive Bayes models for multiple-instance learning with label proportions J Hernández, I Inza Conference of the Spanish Association for Artificial Intelligence, 134-144, 2011 | 8 | 2011 |
| A novel weakly supervised problem: Learning from positive-unlabeled proportions J Hernández-González, I Inza, JA Lozano Conference of the Spanish Association for Artificial Intelligence, 3-13, 2015 | 6 | 2015 |
| Merging knowledge bases in different languages J Hernández-González, ER Hruschka Jr, T Mitchell Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for …, 2017 | 5 | 2017 |
| Similarity networks for heterogeneous data LA Belanche Muñoz, J Hernández González ESANN 2012: the 20th European Symposium on Artificial Neural Networks …, 2012 | 5* | 2012 |
| Beach litter forecasting on the south-eastern coast of the Bay of Biscay: a bayesian networks approach I Granado, OC Basurko, A Rubio, L Ferrer, J Hernández-González, ... Continental Shelf Research 180, 14-23, 2019 | 4 | 2019 |
| Learning naive Bayes models for multiple-instance learning with label proportions J Hernández-González, I Inza | 3 | 2011 |
| A note on the behavior of majority voting in multi-class domains with biased annotators J Hernández-González, I Inza, JA Lozano IEEE Transactions on Knowledge and Data Engineering 31 (1), 195-200, 2018 | 2 | 2018 |
| Two datasets of defect reports labeled by a crowd of annotators of unknown reliability J Hernández-González, D Rodriguez, I Inza, R Harrison, JA Lozano Data in brief 18, 840-845, 2018 | 2 | 2018 |
| Weak Labeling for Crowd Learning I Benaran-Munoz, J Hernández-González, A Pérez ArXiv e-prints, 2018 | 2 | 2018 |
| Learning from crowds in multi-dimensional classification domains J Hernández-González, I Inza, JA Lozano Conference of the Spanish Association for Artificial Intelligence, 352-362, 2013 | 2 | 2013 |
| Aggregated outputs by linear models: An application on marine litter beaching prediction J Hernández-González, I Inza, I Granado, OC Basurko, JA Fernandes, ... Information Sciences 481, 381-393, 2019 | 1 | 2019 |
| Variational Importance Sampling: Initial Findings. J Hernández-González, J Capdevila, J Cerquides CCIA, 95-104, 2019 | 1 | 2019 |
| Evaluation in learning from label proportions: An approximation to the precision-recall curve J Hernández-González Conference of the Spanish Association for Artificial Intelligence, 76-86, 2018 | 1 | 2018 |
| Similarity networks for classification: a case study in the Horse Colic problem L Belanche, J Hernández arXiv preprint arXiv:1403.4540, 2014 | 1 | 2014 |
Inaki InzaIntelligent Systems Group ISG, Computer Science Faculty, University of the Basque Country UPV/EHUVerified email at ehu.es
