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Juan José del Coz
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2020 – today
- 2024
- [j29]Alberto Castaño, Jaime Alonso, Pablo González
, Pablo Pérez
QuantificationLib: A Python library for quantification and prevalence estimation. SoftwareX 26: 101728 (2024) - [j28]Alberto Castaño
Matching Distributions Algorithms Based on the Earth Mover's Distance for Ordinal Quantification. IEEE Trans. Neural Networks Learn. Syst. 35(1): 1050-1061 (2024) - [i3]Alejandro Moreo, Pablo González, Juan José del Coz:
Kernel Density Estimation for Multiclass Quantification. CoRR abs/2401.00490 (2024) - [i2]Olaya Pérez-Mon, Alejandro Moreo, Juan José del Coz, Pablo González:
Quantification using Permutation-Invariant Networks based on Histograms. CoRR abs/2403.15123 (2024) - 2023
- [c22]Alberto Castaño, Jaime Alonso
An Equivalence Analysis of Binary Quantification Methods. AAAI 2023: 6944-6952 - 2022
- [j27]Juan José del Coz, Pablo González, Alejandro Moreo, Fabrizio Sebastiani:
Report on the 1st International Workshop on Learning to Quantify (LQ 2021). SIGKDD Explor. 24(1): 49-51 (2022) - [c21]Juan José del Coz:
UniOviedo(Team2) at LeQua 2022: Comparison of traditional quantifiers and a new method based on Energy Distance. CLEF (Working Notes) 2022: 1869-1874 - 2021
- [c20]Juan José del Coz, Pablo González
Learning to Quantify: Methods and Applications (LQ 2021). CIKM 2021: 4874-4875 - [c19]Pablo González, Juan José del Coz:
Histogram-based Deep Neural Network for Quantification. CIKM Workshops 2021 - 2020
- [j26]Miriam Fdez-Díaz, Laura Fdez-Díaz, Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz:
Improving the ε-approximate algorithm for Probabilistic Classifier Chains. Knowl. Inf. Syst. 62(7): 2709-2738 (2020)
2010 – 2019
- 2019
- [j25]Pablo Pérez-Gállego, Alberto Castaño
Dynamic ensemble selection for quantification tasks. Inf. Fusion 45: 1-15 (2019) - [i1]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
Rectifying Classifier Chains for Multi-Label Classification. CoRR abs/1906.02915 (2019) - 2018
- [j24]Shu-Chao Pang
Deep Learning and Preference Learning for Object Tracking: A Combined Approach. Neural Process. Lett. 47(3): 859-876 (2018) - 2017
- [j23]Pablo González
A Review on Quantification Learning. ACM Comput. Surv. 50(5): 74:1-74:40 (2017) - [j22]Shu-Chao Pang
Deep learning to frame objects for visual target tracking. Eng. Appl. Artif. Intell. 65: 406-420 (2017) - [j21]Pablo Pérez-Gállego
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification. Inf. Fusion 34: 87-100 (2017) - [j20]Deiner Mena, José Ramón Quevedo, Elena Montañés, Juan José del Coz:
A heuristic in A* for inference in nonlinear Probabilistic Classifier Chains. Knowl. Based Syst. 126: 78-90 (2017) - [j19]Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz
A family of admissible heuristics for A* to perform inference in probabilistic classifier chains. Mach. Learn. 106(1): 143-169 (2017) - [j18]Pablo González
Why is quantification an interesting learning problem? Prog. Artif. Intell. 6(1): 53-58 (2017) - 2016
- [j17]Jorge Díez, Juan José del Coz
Using tensor products to detect unconditional label dependence in multilabel classifications. Inf. Sci. 329: 20-32 (2016) - [j16]Enrique J. deAndrés-Galiana
Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems. J. Biomed. Informatics 60: 342-351 (2016) - [j15]Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz
An overview of inference methods in probabilistic classifier chains for multilabel classification. WIREs Data Mining Knowl. Discov. 6(6): 215-230 (2016) - [c18]Shu-Chao Pang
Combining Deep Learning and Preference Learning for Object Tracking. ICONIP (3) 2016: 70-77 - 2015
- [j14]Jorge Díez, Oscar Luaces
Optimizing different loss functions in multilabel classifications. Prog. Artif. Intell. 3(2): 107-118 (2015) - [j13]José Barranquero
Quantification-oriented learning based on reliable classifiers. Pattern Recognit. 48(2): 591-604 (2015) - [c17]Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz:
Using A* for Inference in Probabilistic Classifier Chains. IJCAI 2015: 3707-3713 - 2014
- [j12]Elena Montañés, Robin Senge, José Barranquero
Dependent binary relevance models for multi-label classification. Pattern Recognit. 47(3): 1494-1508 (2014) - 2013
- [j11]Elena Montañés
Enhancing directed binary trees for multi-class classification. Inf. Sci. 223: 42-55 (2013) - [j10]José Barranquero
On the study of nearest neighbor algorithms for prevalence estimation in binary problems. Pattern Recognit. 46(2): 472-482 (2013) - [j9]Pablo González
Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation. IEEE Trans. Neural Networks Learn. Syst. 24(11): 1901-1905 (2013) - [c16]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
Rectifying Classifier Chains for Multi-Label Classification. LWA 2013: 151-158 - 2012
- [j8]Juan José del Coz
Learning data structure from classes: A case study applied to population genetics. Inf. Sci. 193: 22-35 (2012) - [j7]Oscar Luaces
Binary relevance efficacy for multilabel classification. Prog. Artif. Intell. 1(4): 303-313 (2012) - [c15]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. GfKl 2012: 163-170 - 2011
- [c14]Elena Montañés
Aggregating Independent and Dependent Models to Learn Multi-label Classifiers. ECML/PKDD (2) 2011: 484-500 - 2010
- [j6]Oscar Luaces
Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. J. Comput. Biol. 17(12): 1711-1723 (2010) - [j5]Jorge Díez, Juan José del Coz
A semi-dependent decomposition approach to learn hierarchical classifiers. Pattern Recognit. 43(11): 3795-3804 (2010) - [c13]José Ramón Quevedo, Elena Montañés
Adapting Decision DAGs for Multipartite Ranking. ECML/PKDD (3) 2010: 115-130
2000 – 2009
- 2009
- [j4]Juan José del Coz, Jorge Díez, Antonio Bahamonde:
Learning Nondeterministic Classifiers. J. Mach. Learn. Res. 10: 2273-2293 (2009) - [c12]Antonio Bahamonde, Jaime Alonso
Prediction and Inheritance of Phenotypes. IWINAC (1) 2009: 275-284 - [c11]Jorge Díez, Juan José del Coz
Soft Margin Trees. ECML/PKDD (1) 2009: 302-314 - 2008
- [j3]Jorge Díez, Juan José del Coz
Clustering people according to their preference criteria. Expert Syst. Appl. 34(2): 1274-1284 (2008) - [c10]Jaime Alonso
Learning to Predict One or More Ranks in Ordinal Regression Tasks. ECML/PKDD (1) 2008: 39-54 - 2006
- [c9]Juan José del Coz, Jorge Díez, Antonio Bahamonde, Carlos Sañudo, Matilde Alfonso, Philippe Berge, Eric Dransfield, Costas Stamataris, Demetrios Zygoyiannis, Tyri Valdimarsdottir, Edi Piasentier, Geoffrey Nute, Alan Fisher:
Learning the Reasons Why Groups of Consumers Prefer Some Food Products. ICDM 2006: 297-309 - 2005
- [c8]Jorge Díez, Juan José del Coz, Carlos Sañudo, Pere Albertí, Antonio Bahamonde:
A Kernel Based Method for Discovering Market Segments in Beef Meat. PKDD 2005: 462-469 - 2004
- [c7]Jorge Díez, Gustavo F. Bayón, José Ramón Quevedo, Juan José del Coz, Oscar Luaces, Jaime Alonso, Antonio Bahamonde:
Discovering Relevancies in Very Difficult Regression Problems: Applications to Sensory Data Analysis. ECAI 2004: 993-994 - [c6]Oscar Luaces, Gustavo F. Bayón, José Ramón Quevedo, Jorge Díez, Juan José del Coz, Antonio Bahamonde:
Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection. ECML 2004: 286-297 - [c5]Antonio Bahamonde, Gustavo F. Bayón, Jorge Díez, José Ramón Quevedo, Oscar Luaces, Juan José del Coz, Jaime Alonso
Feature subset selection for learning preferences: a case study. ICML 2004 - [c4]Juan José del Coz, Gustavo F. Bayón, Jorge Díez, Oscar Luaces, Antonio Bahamonde, Carlos Sañudo:
Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. NIPS 2004: 321-328 - 2002
- [c3]Jorge Díez, Juan José del Coz, Oscar Luaces
Learning to Assess from Pair-Wise Comparisons. IBERAMIA 2002: 481-490 - 2000
- [j2]F. López, Félix Goyache, José Ramón Quevedo, Jaime Alonso, José Ranilla, Oscar Luaces, Antonio Bahamonde, Juan José del Coz:
Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. Inteligencia Artif. 4(10): 5-17 (2000) - [j1]José Marcos Alonso Alvarez, Javier Ribas Bueno
Development of a distributive control scheme for fluorescent lighting based on LonWorks technology. IEEE Trans. Ind. Electron. 47(6): 1253-1262 (2000)
1990 – 1999
- 1999
- [c2]Oscar Luaces
Autonomous Clustering for Machine Learning. IWANN (1) 1999: 497-506 - [c1]Juan José del Coz
Self-Organizing Cases to Find Paradigms. IWANN (1) 1999: 527-536
Coauthor Index
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last updated on 2024-08-05 20:22 CEST by the dblp team
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