JR Quevedo
JR Quevedo
Dirección de correo verificada de aic.uniovi.es
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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
992014
Multilabel classifiers with a probabilistic thresholding strategy
JR Quevedo, O Luaces, A Bahamonde
Pattern Recognition 45 (2), 876-883, 2012
652012
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
602004
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
552001
Graphical feature selection for multilabel classification tasks
G Lastra, O Luaces, JR Quevedo, A Bahamonde
International Symposium on Intelligent Data Analysis, 246-257, 2011
382011
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
362003
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
322007
Genetical genomics: use all data
M Pérez-Enciso, JR Quevedo, A Bahamonde
BMC genomics 8 (1), 69, 2007
312007
Aggregating independent and dependent models to learn multi-label classifiers
E Montañés, JR Quevedo, JJ del Coz
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011
302011
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
European Conference on Machine Learning, 286-297, 2004
302004
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
252017
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
232004
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
232001
Self-organizing cases to find paradigms
JJ Del Coz, O Luaces, JR Quevedo, J Alonso, J Ranilla, A Bahamonde
International Work-Conference on Artificial Neural Networks, 527-536, 1999
181999
Using A* for inference in probabilistic classifier chains
D Mena, E Montañés, JR Quevedo, JJ Del Coz
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
172015
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
172007
A wrapper approach with support vector machines for text categorization
E Montañés, JR Quevedo, I Díaz
International Work-Conference on Artificial Neural Networks, 230-237, 2003
172003
Forecasting time series combining machine learning and box-jenkins time series
E Montañés, JR Quevedo, MM Prieto, CO Menéndez
Ibero-American Conference on Artificial Intelligence, 491-499, 2002
172002
Viability of an alarm predictor for coffee rust disease using interval regression
O Luaces, LHA Rodrigues, CAA Meira, JR Quevedo, A Bahamonde
International Conference on Industrial, Engineering and Other Applications …, 2010
162010
Collaborative tag recommendation system based on logistic regression
E Montañés, JR Quevedo, I Díaz, J Ranilla
ECML PKDD Discovery Challenge, 173-188, 2009
142009
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Artículos 1–20