JR Quevedo
JR Quevedo
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Dependent binary relevance models for multi-label classification
E Montañes, R Senge, J Barranquero, JR Quevedo, JJ del Coz, ...
Pattern Recognition 47 (3), 1494-1508, 2014
Multilabel classifiers with a probabilistic thresholding strategy
JR Quevedo, O Luaces, A Bahamonde
Pattern Recognition 45 (2), 876-883, 2012
The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry
F Goyache, A Bahamonde, J Alonso, S López, JJ Del Coz, JR Quevedo, ...
Trends in Food Science & Technology 12 (10), 370-381, 2001
Feature subset selection for learning preferences: A case study
A Bahamonde, GF Bayón, J Díez, JR Quevedo, O Luaces, JJ Del Coz, ...
Proceedings of the twenty-first international conference on Machine learning, 7, 2004
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification
P Pérez-Gállego, JR Quevedo, JJ del Coz
Information Fusion 34, 87-100, 2017
Graphical feature selection for multilabel classification tasks
G Lastra, O Luaces, JR Quevedo, A Bahamonde
Advances in Intelligent Data Analysis X: 10th International Symposium, IDA …, 2011
Using artificial intelligence to design and implement a morphological assessment system in beef cattle
F Goyache, JJ Del Coz, JR Quevedo, S López, J Alonso, J Ranilla, ...
Animal Science 73 (1), 49-60, 2001
Dynamic ensemble selection for quantification tasks
P Pérez-Gállego, A Castano, JR Quevedo, JJ del Coz
Information Fusion 45, 1-15, 2019
How to learn consumer preferences from the analysis of sensory data by means of support vector machines (SVM)
A Bahamonde, J Díez, JR Quevedo, O Luaces, JJ del Coz
Trends in food science & technology 18 (1), 20-28, 2007
Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses
J Dıez, A Bahamonde, J Alonso, S López, JJ Del Coz, JR Quevedo, ...
Meat Science 64 (3), 249-258, 2003
Analyzing sensory data using non-linear preference learning with feature subset selection
O Luaces, GF Bayón, JR Quevedo, J Díez, JJ Del Coz, A Bahamonde
Machine Learning: ECML 2004: 15th European Conference on Machine Learning …, 2004
Genetical genomics: use all data
M Pérez-Enciso, JR Quevedo, A Bahamonde
BMC genomics 8 (1), 1-8, 2007
Aggregating independent and dependent models to learn multi-label classifiers
E Montanés, JR Quevedo, JJ del Coz
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
An overview of inference methods in probabilistic classifier chains for multilabel classification
D Mena, E Montañés, JR Quevedo, JJ del Coz
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6 (6 …, 2016
Discovering relevancies in very difficult regression problems: applications to sensory data analysis
J Díez Peláez, G Fernández Bayón, JR Quevedo Pérez, JJ Coz Velasco, ...
Proceedings of the European conference on artificial intelligence (ECAI’04), 2004
Using A* for inference in probabilistic classifier chains
D Mena Waldo, E Montañés Roces, JR Quevedo Pérez, JJ Coz Velasco
Proceedings of the Twenty-Fourth International Joint Conference on …, 2015
A wrapper approach with support vector machines for text categorization
E Montañés, JR Quevedo, I Díaz
Computational Methods in Neural Modeling: 7th International Work-Conference …, 2003
Viability of an alarm predictor for coffee rust disease using interval regression
O Luaces, LHA Rodrigues, CA Alves Meira, JR Quevedo, A Bahamonde
Trends in Applied Intelligent Systems: 23rd International Conference on …, 2010
Collaborative tag recommendation system based on logistic regression
E Montanés, JR Quevedo, I Díaz, J Ranilla
ECML PKDD Discovery Challenge, 173-188, 2009
A simple and efficient method for variable ranking according to their usefulness for learning
JR Quevedo, A Bahamonde, O Luaces
Computational statistics & data analysis 52 (1), 578-595, 2007
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