J. J. D. Coz

发表

How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM)

José Ramón Quevedo, Juan José del Coz, Oscar Luaces, 2007 .

Improving the ε-approximate algorithm for Probabilistic Classifier Chains

José Ramón Quevedo, Juan José del Coz, J. R. Quevedo, 2020, Knowl. Inf. Syst..

Rectifying Classifier Chains for Multi-Label Classification pdf

Eyke Hüllermeier, Robin Senge, Juan José del Coz, 2019, LWA.

An overview of inference methods in probabilistic classifier chains for multilabel classification

Elena Montañés, José Ramón Quevedo, Juan José del Coz, 2016, WIREs Data Mining Knowl. Discov..

Dependent binary relevance models for multi-label classification

Eyke Hüllermeier, Elena Montañés, José Ramón Quevedo, 2014, Pattern Recognit..

Aggregating Independent and Dependent Models to Learn Multi-label Classifiers

Elena Montañés, José Ramón Quevedo, Juan José del Coz, 2011, ECML/PKDD.

Dynamic ensemble selection for quantification tasks

José Ramón Quevedo, Juan José del Coz, J. R. Quevedo, 2019, Inf. Fusion.

Using A* for Inference in Probabilistic Classifier Chains

Elena Montañés, José Ramón Quevedo, Juan José del Coz, 2015, IJCAI.

Learning data structure from classes: A case study applied to population genetics

Juan José del Coz, Jorge Díez, Antonio Bahamonde, 2012, Inf. Sci..

Soft Margin Trees

Juan José del Coz, Oscar Luaces, Jorge Díez, 2009, ECML/PKDD.

Learning to Predict One or More Ranks in Ordinal Regression Tasks

Juan José del Coz, Oscar Luaces, Jorge Díez, 2008, ECML/PKDD.

Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models

José Ramón Quevedo, Juan José del Coz, Oscar Luaces, 2010, J. Comput. Biol..

Prediction and Inheritance of Phenotypes

José Ramón Quevedo, Juan José del Coz, Oscar Luaces, 2009, IWINAC.

Deep learning to frame objects for visual target tracking

Zhezhou Yu, Shu-Chao Pang, Juan José del Coz, 2017, Eng. Appl. Artif. Intell..

Binary relevance efficacy for multilabel classification

Juan José del Coz, Oscar Luaces, Antonio Bahamonde, 2012, Progress in Artificial Intelligence.

Identifying market segments in beef: Breed, slaughter weight and ageing time implications.

J. Díez, A. Bahamonde, B. Panea, 2006, Meat science.

A Review on Quantification Learning

Nitesh V. Chawla, Juan José del Coz, Pablo González, 2017, ACM Comput. Surv..

Learning Nondeterministic Classifiers

Juan José del Coz, Jorge Díez, Antonio Bahamonde, 2009, J. Mach. Learn. Res..

Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM

Juan José del Coz, Oscar Luaces, Jorge Díez, 2004, NIPS.

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

José Ramón Quevedo, Juan José del Coz, Oscar Luaces, 2004, ECML.

Feature subset selection for learning preferences: a case study

José Ramón Quevedo, Juan José del Coz, Oscar Luaces, 2004, ICML.

A family of admissible heuristics for A* to perform inference in probabilistic classifier chains

Elena Montañés, José Ramón Quevedo, Juan José del Coz, 2016, Machine Learning.

On the Problem of Error Propagation in Classifier Chains for Multi-label Classification

Eyke Hüllermeier, Robin Senge, Juan José del Coz, 2012, GfKl.

A semi-dependent decomposition approach to learn hierarchical classifiers

Juan José del Coz, Jorge Díez, Antonio Bahamonde, 2010, Pattern Recognit..

Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems

Enrique J. deAndrés-Galiana, Oscar Luaces, Juan Luis Fernández Martínez, 2016, J. Biomed. Informatics.

Using ensembles for problems with characterizable changes in data distribution: A case study on quantification

José Ramón Quevedo, Juan José del Coz, J. R. Quevedo, 2017, Inf. Fusion.

Why is quantification an interesting learning problem?

Nitesh V. Chawla, Juan José del Coz, Jorge Díez, 2016, Progress in Artificial Intelligence.

Quantification-oriented learning based on reliable classifiers

Juan José del Coz, Jorge Díez, José Barranquero, 2015, Pattern Recognit..

On the study of nearest neighbor algorithms for prevalence estimation in binary problems

Juan José del Coz, Jorge Díez, Pablo González, 2013, Pattern Recognit..