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ORCIDJerónimo Hernández-González
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2020 – today
- 2024
[c11]Vien Ngoc Dang, Adrià Casamitjana, Jerónimo Hernández-González, Karim Lekadir, Alzheimer's Disease Neuroimaging Initiative:
Mitigating Overdiagnosis Bias in CNN-Based Alzheimer's Disease Diagnosis for the Elderly. FAIMI/EPIMI@MICCAI 2024: 46-55- 2023
- [j15]Anna Cascarano
, Jordi Mur-Petit, Jerónimo Hernández-González, Marina Camacho, Nina de Toro Eadie, Polyxeni Gkontra, Marc Chadeau-Hyam, Jordi Vitrià, Karim Lekadir:
Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artif. Intell. Rev. 56(S2): 1711-1771 (2023) - [j14]Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez:
On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels. Knowl. Inf. Syst. 65(1): 241-260 (2023) - [j13]Jerónimo Hernández-González, Pedro Javier Herrera:
On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique. IEEE Trans. Learn. Technol. 16(6): 926-939 (2023) - [c10]Vien Ngoc Dang, Adrià Casamitjana, Martijn P. A. Starmans, Carlos Martín-Isla, Jerónimo Hernández-González, Karim Lekadir, Alzheimer's Disease Neuroimaging Initiative:
Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer's Disease. CLIP/FAIMI/EPIMI@MICCAI 2023: 172-182 - 2022
- [j12]Jerónimo Hernández-González, Olga Valls, Adrián Torres-Martín, Jesús Cerquides:
Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models. Comput. Biol. Medicine 150: 106160 (2022) - [j11]Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez:
Machine Learning From Crowds Using Candidate Set-Based Labeling. IEEE Intell. Syst. 37(6): 57-68 (2022) - [j10]Jerónimo Hernández-González, Aritz Pérez:
On the relative value of weak information of supervision for learning generative models: An empirical study. Int. J. Approx. Reason. 150: 258-272 (2022) - [i3]Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir:
Fairness and bias correction in machine learning for depression prediction: results from four different study populations. CoRR abs/2211.05321 (2022) - 2021
- [c9]Adrián Torres-Martín, Jerónimo Hernández-González, Jesús Cerquides:
Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection. CCIA 2021: 225-234 - 2020
- [j9]Jerónimo Hernández-González, Jesús Cerquides:
A Robust Solution to Variational Importance Sampling of Minimum Variance. Entropy 22(12): 1405 (2020)
2010 – 2019
- 2019
- [j8]Jerónimo Hernández-González, Iñaki Inza, Igor Granado, Oihane C. Basurko, Jose A. Fernandes, José Antonio Lozano:
Aggregated outputs by linear models: An application on marine litter beaching prediction. Inf. Sci. 481: 381-393 (2019) - [j7]Jerónimo Hernández-González:
A framework for evaluation in learning from label proportions. Prog. Artif. Intell. 8(3): 359-373 (2019) - [j6]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators. IEEE Trans. Knowl. Data Eng. 31(1): 195-200 (2019) - [c8]Jerónimo Hernández-González, Joan Capdevila, Jesús Cerquides:
Variational Importance Sampling: Initial Findings. CCIA 2019: 95-104 - 2018
- [j5]Jerónimo Hernández-González, Daniel Rodríguez, Iñaki Inza, Rachel Harrison, José Antonio Lozano:
Learning to classify software defects from crowds: A novel approach. Appl. Soft Comput. 62: 579-591 (2018) - [c7]Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez:
Crowd Learning with Candidate Labeling: An EM-Based Solution. CAEPIA 2018: 13-23 - [c6]Jerónimo Hernández-González:
Evaluation in Learning from Label Proportions: An Approximation to the Precision-Recall Curve. CAEPIA 2018: 76-86 - [i2]Iker Beñaran-Muñoz, Jerónimo Hernández-González, Aritz Pérez:
Weak Labeling for Crowd Learning. CoRR abs/1804.10023 (2018) - 2017
- [j4]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
Learning from Proportions of Positive and Unlabeled Examples. Int. J. Intell. Syst. 32(2): 109-133 (2017) - [c5]Jerónimo Hernández-González, Estevam R. Hruschka Jr., Tom M. Mitchell:
Merging knowledge bases in different languages. TextGraphs@ACL 2017: 21-29 - 2016
- [j3]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
Weak supervision and other non-standard classification problems: A taxonomy. Pattern Recognit. Lett. 69: 49-55 (2016) - 2015
- [j2]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection. Int. J. Intell. Syst. 30(3): 326-354 (2015) - [c4]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
A Novel Weakly Supervised Problem: Learning from Positive-Unlabeled Proportions. CAEPIA 2015: 3-13 - 2014
- [i1]Lluís Belanche, Jerónimo Hernández-González:
Similarity networks for classification: a case study in the Horse Colic problem. CoRR abs/1403.4540 (2014) - 2013
- [j1]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
Learning Bayesian network classifiers from label proportions. Pattern Recognit. 46(12): 3425-3440 (2013) - [c3]Jerónimo Hernández-González, Iñaki Inza, José Antonio Lozano:
Learning from Crowds in Multi-dimensional Classification Domains. CAEPIA 2013: 352-362 - 2012
- [c2]Lluís Belanche, Jerónimo Hernández-González:
Similarity networks for heterogeneous data. ESANN 2012 - 2011
- [c1]Jerónimo Hernández-González, Iñaki Inza:
Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions. CAEPIA 2011: 134-144
Coauthor Index
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