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Jerónimo Hernández-González
Jerónimo Hernández-González
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Title
Cited by
Cited by
Year
Weak supervision and other non-standard classification problems: a taxonomy
J Hernández-González, I Inza, JA Lozano
Pattern Recognition Letters 69, 49-55, 2016
1212016
Learning Bayesian network classifiers from label proportions
J Hernández-González, I Inza, JA Lozano
Pattern Recognition 46 (12), 3425-3440, 2013
832013
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
402018
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
342018
A note on the behavior of majority voting in multi-class domains with biased annotators
J Hernandez-Gonzalez, I Inza, JA Lozano
IEEE Transactions on Knowledge and Data Engineering 31 (1), 195-200, 2018
182018
A conceptual probabilistic framework for annotation aggregation of citizen science data
J Cerquides, MO Mülâyim, J Hernández-González, A Ravi Shankar, ...
Mathematics 9 (8), 875, 2021
152021
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
122019
Learning naive Bayes models for multiple-instance learning with label proportions
J Hernández, I Inza
Advances in Artificial Intelligence: 14th Conference of the Spanish …, 2011
122011
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
112017
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
112015
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
82019
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
72017
Similarity networks for heterogeneous data
LA Belanche Muñoz, J Hernández González
ESANN 2012: the 20th European Symposium on Artificial Neural Networks …, 2012
72012
A novel weakly supervised problem: Learning from positive-unlabeled proportions
J Hernández-González, I Inza, JA Lozano
Advances in Artificial Intelligence: 16th Conference of the Spanish …, 2015
62015
Machine and deep learning for longitudinal biomedical data: a review of methods and applications
A Cascarano, J Mur-Petit, J Hernandez-Gonzalez, M Camacho, ...
Artificial Intelligence Review 56 (Suppl 2), 1711-1771, 2023
52023
Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study
J Villar, JM González-Martín, J Hernández-González, MA Armengol, ...
Critical care medicine, 2023
42023
A robust solution to variational importance sampling of minimum variance
J Hernández-González, J Cerquides
Entropy 22 (12), 1405, 2020
42020
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
42018
Weak labeling for crowd learning
I Benaran-Munoz, J Hernández-González, A Pérez
ArXiv e-prints, 2018
32018
On the relative value of weak information of supervision for learning generative models: An empirical study
J Hernández-González, A Pérez
International Journal of Approximate Reasoning 150, 258-272, 2022
22022
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Articles 1–20