Self-Organizing Cases to Find Paradigms. - Juan José del Coz; Oscar Luaces; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Antonio Bahamonde (IWANN (1), 1999)

Autonomous Clustering for Machine Learning. - Oscar Luaces; Juan José del Coz; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Antonio Bahamonde (IWANN (1), 1999)

Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. - F. López; Félix Goyache; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Oscar Luaces; Antonio Bahamonde; Juan José del Coz (Inteligencia Artif., 2000)

Development of a distributive control scheme for fluorescent lighting based on LonWorks technology. - José Marcos Alonso Alvarez; Javier Ribas Bueno; Juan José del Coz Velasco; Antonio Javier Calleja Rodriguez; Emilio López Corominas; Manuel Rico-Secades (IEEE Trans. Ind. Electron., 2000)

Learning to Assess from Pair-Wise Comparisons. - Jorge Díez 001; Juan José del Coz; Oscar Luaces; Félix Goyache; Jaime Alonso 001; A. M. Peña; Antonio Bahamonde (IBERAMIA, 2002)

Discovering Relevancies in Very Difficult Regression Problems: Applications to Sensory Data Analysis. - Jorge Díez 001; Gustavo F. Bayón; José Ramón Quevedo; Juan José del Coz; Oscar Luaces; Jaime Alonso 001; Antonio Bahamonde (ECAI, 2004)

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection. - Oscar Luaces; Gustavo F. Bayón; José Ramón Quevedo; Jorge Díez 001; Juan José del Coz; Antonio Bahamonde (ECML, 2004)

Feature subset selection for learning preferences: a case study. - Antonio Bahamonde; Gustavo F. Bayón; Jorge Díez 001; José Ramón Quevedo; Oscar Luaces; Juan José del Coz; Jaime Alonso 001; Félix Goyache (ICML, 2004)

Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. - Juan José del Coz; Gustavo F. Bayón; Jorge Díez 001; Oscar Luaces; Antonio Bahamonde; Carlos Sañudo (NIPS, 2004)

A Kernel Based Method for Discovering Market Segments in Beef Meat. - Jorge Díez 001; Juan José del Coz; Carlos Sañudo; Pere Albertí; Antonio Bahamonde (PKDD, 2005)

Learning the Reasons Why Groups of Consumers Prefer Some Food Products. - Juan José del Coz; Jorge Díez 001; Antonio Bahamonde; Carlos Sañudo; Matilde Alfonso; Philippe Berge; Eric Dransfield; Costas Stamataris; Demetrios Zygoyiannis; Tyri Valdimarsdottir; Edi Piasentier; Geoffrey Nute; et al. (ICDM, 2006)

Learning to Predict One or More Ranks in Ordinal Regression Tasks. - Jaime Alonso 001; Juan José del Coz; Jorge Díez 001; Oscar Luaces; Antonio Bahamonde (ECML/PKDD (1), 2008)

Clustering people according to their preference criteria. - Jorge Díez 001; Juan José del Coz; Oscar Luaces; Antonio Bahamonde (Expert Syst. Appl., 2008)

Prediction and Inheritance of Phenotypes. - Antonio Bahamonde; Jaime Alonso 001; Juan José del Coz; Jorge Díez 001; José Ramón Quevedo; Oscar Luaces (IWINAC (1), 2009)

Soft Margin Trees. - Jorge Díez 001; Juan José del Coz; Antonio Bahamonde; Oscar Luaces (ECML/PKDD (1), 2009)

Learning Nondeterministic Classifiers. - Juan José del Coz; Jorge Díez 001; Antonio Bahamonde (J. Mach. Learn. Res., 2009)

Adapting Decision DAGs for Multipartite Ranking. - José Ramón Quevedo; Elena Montañés; Oscar Luaces; Juan José del Coz (ECML/PKDD (3), 2010)

Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. - Oscar Luaces; José Ramón Quevedo; Miguel Pérez-Enciso; Jorge Díez 001; Juan José del Coz; Antonio Bahamonde (J. Comput. Biol., 2010)

A semi-dependent decomposition approach to learn hierarchical classifiers. - Jorge Díez 001; Juan José del Coz; Antonio Bahamonde (Pattern Recognit., 2010)

Aggregating Independent and Dependent Models to Learn Multi-label Classifiers. - Elena Montañés; José Ramón Quevedo; Juan José del Coz (ECML/PKDD (2), 2011)

On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. - Robin Senge; Juan José del Coz; Eyke Hüllermeier (GfKl, 2012)

Learning data structure from classes: A case study applied to population genetics. - Juan José del Coz; Jorge Díez 001; Antonio Bahamonde; Félix Goyache (Inf. Sci., 2012)

Binary relevance efficacy for multilabel classification. - Oscar Luaces; Jorge Díez 001; José Barranquero; Juan José del Coz; Antonio Bahamonde (Prog. Artif. Intell., 2012)

Rectifying Classifier Chains for Multi-Label Classification. - Robin Senge; Juan José del Coz; Eyke Hüllermeier (LWA, 2013)

Enhancing directed binary trees for multi-class classification. - Elena Montañés; José Barranquero; Jorge Díez 001; Juan José del Coz (Inf. Sci., 2013)

On the study of nearest neighbor algorithms for prevalence estimation in binary problems. - José Barranquero; Pablo González; Jorge Díez 001; Juan José del Coz (Pattern Recognit., 2013)

Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation. - Pablo González; Eva Alvarez; José Barranquero; Jorge Díez 001; Rafael Gonzalez-Quiros; Enrique Nogueira; Angel Lopez-Urrutia; Juan José del Coz (IEEE Trans. Neural Networks Learn. Syst., 2013)

Dependent binary relevance models for multi-label classification. - Elena Montañés; Robin Senge; José Barranquero; José Ramón Quevedo; Juan José del Coz; Eyke Hüllermeier (Pattern Recognit., 2014)

Using A* for Inference in Probabilistic Classifier Chains. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (IJCAI, 2015)

Optimizing different loss functions in multilabel classifications. - Jorge Díez 001; Oscar Luaces; Juan José del Coz; Antonio Bahamonde (Prog. Artif. Intell., 2015)

Quantification-oriented learning based on reliable classifiers. - José Barranquero; Jorge Díez 001; Juan José del Coz (Pattern Recognit., 2015)

Combining Deep Learning and Preference Learning for Object Tracking. - Shu-Chao Pang; Juan José del Coz; Zhezhou Yu; Oscar Luaces; Jorge Díez 001 (ICONIP (3), 2016)

Using tensor products to detect unconditional label dependence in multilabel classifications. - Jorge Díez 001; Juan José del Coz; Oscar Luaces; Antonio Bahamonde (Inf. Sci., 2016)

Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems. - Enrique J. deAndrés-Galiana; Juan Luis Fernández Martínez; Oscar Luaces; Juan José del Coz; Leticia Huergo-Zapico; Andrea Acebes-Huerta; Segundo González; Ana P. González-Rodríguez (J. Biomed. Informatics, 2016)

An overview of inference methods in probabilistic classifier chains for multilabel classification. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (WIREs Data Mining Knowl. Discov., 2016)

A Review on Quantification Learning. - Pablo González; Alberto Castaño; Nitesh V. Chawla; Juan José del Coz (ACM Comput. Surv., 2017)

Deep learning to frame objects for visual target tracking. - Shu-Chao Pang; Juan José del Coz; Zhezhou Yu; Oscar Luaces; Jorge Díez 001 (Eng. Appl. Artif. Intell., 2017)

Using ensembles for problems with characterizable changes in data distribution: A case study on quantification. - Pablo Pérez-Gállego; José Ramón Quevedo; Juan José del Coz (Inf. Fusion, 2017)

A heuristic in A* for inference in nonlinear Probabilistic Classifier Chains. - Deiner Mena; José Ramón Quevedo; Elena Montañés; Juan José del Coz (Knowl. Based Syst., 2017)

A family of admissible heuristics for A* to perform inference in probabilistic classifier chains. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (Mach. Learn., 2017)

Why is quantification an interesting learning problem? - Pablo González; Jorge Díez 001; Nitesh V. Chawla; Juan José del Coz (Prog. Artif. Intell., 2017)

Deep Learning and Preference Learning for Object Tracking: A Combined Approach. - Shu-Chao Pang; Juan José del Coz; Zhezhou Yu; Oscar Luaces; Jorge Díez 001 (Neural Process. Lett., 2018)

Dynamic ensemble selection for quantification tasks. - Pablo Pérez-Gállego; Alberto Castaño; José Ramón Quevedo; Juan José del Coz (Inf. Fusion, 2019)

Improving the ε-approximate algorithm for Probabilistic Classifier Chains. - Miriam Fdez-Díaz; Laura Fdez-Díaz; Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (Knowl. Inf. Syst., 2020)

Learning to Quantify: Methods and Applications (LQ 2021). - Juan José del Coz; Pablo González; Alejandro Moreo; Fabrizio Sebastiani 001 (CIKM, 2021)

Histogram-based Deep Neural Network for Quantification. - Pablo González; Juan José del Coz (CIKM Workshops, 2021)

UniOviedo(Team2) at LeQua 2022: Comparison of traditional quantifiers and a new method based on Energy Distance. - Juan José del Coz (CLEF (Working Notes), 2022)

Report on the 1st International Workshop on Learning to Quantify (LQ 2021). - Juan José del Coz; Pablo González; Alejandro Moreo; Fabrizio Sebastiani 001 (SIGKDD Explor., 2022)

An Equivalence Analysis of Binary Quantification Methods. - Alberto Castaño; Jaime Alonso 001; Pablo González; Juan José del Coz (AAAI, 2023)

QuantificationLib: A Python library for quantification and prevalence estimation. - Alberto Castaño; Jaime Alonso 001; Pablo González; Pablo Pérez; Juan José del Coz (SoftwareX, 2024)

Matching Distributions Algorithms Based on the Earth Mover's Distance for Ordinal Quantification. - Alberto Castaño; Pablo González; Jaime Alonso González 001; Juan José del Coz (IEEE Trans. Neural Networks Learn. Syst., 2024)

Quantification via Gaussian Latent Space Representations. - Olaya Pérez-Mon; Juan José del Coz; Pablo González (CoRR, 2025)

Quantification using permutation-invariant networks based on histograms. - Olaya Pérez-Mon; Alejandro Moreo; Juan José del Coz; Pablo González (Neural Comput. Appl., 2025)

Kernel density estimation for multiclass quantification. - Alejandro Moreo; Pablo González; Juan José del Coz (Mach. Learn., 2025)