J. R. Quevedo

发表

BiomaSoft: sistema informático para el monitoreo y evaluación de la producción de alimentos y energía. Parte I

J. R. Quevedo, J. Suárez, J. R. Quevedo, 2015 .

Improving the Discriminatory Power of a Near-Infrared Microscopy Spectral Library with a Support Vector Machine Classifier

J. R. Quevedo, T. Fearn, V. Fernández-Ibáñez, 2010, Applied spectroscopy.

Capturing waste collection planning expert knowledge in a fitness function through preference learning

José Ramón Quevedo, J. R. Quevedo, Miriam Fdez-Díaz, 2021, Eng. Appl. Artif. Intell..

Genetical genomics: use all data

J. R. Quevedo, A. Bahamonde, M. Pérez-Enciso, 2007, BMC Genomics.

Obtaining Rubric Weights for Assessments by More than One Lecturer Using a Pairwise Learning Model.

Elena Montañés, José Ramón Quevedo, J. R. Quevedo, 2009, EDM 2009.

TagRanker: learning to recommend ranked tags

José Ranilla, Pedro Alonso, Irene Díaz, 2011, Log. J. IGPL.

Validation of two discriminant strategies applied to NIRS data spectra for detection of animal meals in feedstuffs

J. R. Quevedo, A. Garrido-Varo, J. Guerrero, 2011 .

Analyzing superstars’ power using support vector machines

J. R. Quevedo, A. Suárez-Vázquez, 2015, Empirical Economics.

Forecasting Time Series Combining Machine Learning and Box-Jenkins Time Series

Elena Montañés, José Ramón Quevedo, Maria M. Prieto, 2002, IBERAMIA.

Filtering Noisy Continuous Labeled Examples

Elena Montañés, José Ramón Quevedo, J. R. Quevedo, 2002, IBERAMIA.

Analyzing superstars’ power using support vector machines

J. R. Quevedo, Ana Suárez-Vázquez, José R. Quevedo, 2015 .

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.

Predicting fertility from seminal traits: Performance of several parametric and non-parametric procedures

Elena Montañés, José Ramón Quevedo, Jorge Díez, 2013 .

Choosing among Algorithms to Improve Accuracy

Elías F. Combarro, José Ramón Quevedo, Antonio Bahamonde, 2003, IWANN.

The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry

José Ranilla, José Ramón Quevedo, Oscar Luaces, 2001 .

Using artificial intelligence to design and implement a morphological assessment system in beef cattle

José Ranilla, José Ramón Quevedo, Oscar Luaces, 2001 .

Feature subset selection for learning preferences: a case study

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

Improving importance estimation in covariate shift for providing accurate prediction error

J. R. Quevedo, E. Montañés, Laura Fdez-Díaz, 2022, Expert Syst. Appl..

Dynamic ensemble selection for quantification tasks

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

Ordinal classification/regression for analyzing the influence of superstars on spectators in cinema marketing

Elena Montañés, José Ramón Quevedo, J. R. Quevedo, 2014, Expert Syst. Appl..

Adapting Decision DAGs for Multipartite Ranking

Elena Montañés, José Ramón Quevedo, Juan José del Coz, 2010, ECML/PKDD.

Collaborative Tag Recommendation System based on Logistic Regression

José Ranilla, Irene Díaz, Elena Montañés, 2009, DC@PKDD/ECML.

Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve

José Ramón Quevedo, Oscar Luaces, Antonio Bahamonde, 2007, IJCAI.

Target inductive methods for zero-shot regression

J. R. Quevedo, E. Montañés, Miriam Fdez-Díaz, 2022, Inf. Sci..

Multilabel classifiers with a probabilistic thresholding strategy

José Ramón Quevedo, Oscar Luaces, Antonio Bahamonde, 2012, Pattern Recognit..

A greedy algorithm for dimensionality reduction in polynomial regression to forecast the performance of a power plant condenser

Elena Montañés, José Ramón Quevedo, Maria M. Prieto, 2011, Intell. Data Anal..

A Wrapper Approach with Support Vector Machines for Text Categorization

Irene Díaz, Elena Montañés, José Ramón Quevedo, 2003, IWANN.

Improving the ε-approximate algorithm for Probabilistic Classifier Chains

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

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.

Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble

J. R. Quevedo, E. Montañés, Laura Fdez-Díaz, 2023, Neurocomputing.

Viability of an Alarm Predictor for Coffee Rust Disease Using Interval Regression

José Ramón Quevedo, Oscar Luaces, Antonio Bahamonde, 2010, IEA/AIE.

A simple and efficient method for variable ranking according to their usefulness for learning

José Ramón Quevedo, Oscar Luaces, Antonio Bahamonde, 2007, Comput. Stat. Data Anal..

Using A* for Inference in Probabilistic Classifier Chains

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

Waste heat recovery system for marine engines optimized through a preference learning rank function embedded into a Bayesian optimizer

J. R. Quevedo, E. Montañés, N. Rivera, 2023, Ocean Engineering.

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 .

Ranked Tag Recommendation Systems Based on Logistic Regression

José Ranilla, Irene Díaz, Elena Montañés, 2010, HAIS.

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.

A Hybrid Feature Selection Method for Text Categorization

José Ranilla, Irene Díaz, Elías F. Combarro, 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

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

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

J. R. Quevedo, E. Montañés, J. J. del Coz, 2016, Machine Learning.

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.

Parametric study of organic Rankine working fluids via Bayesian optimization of a preference learning ranking for a waste heat recovery system applied to a case study marine engine

J. R. Quevedo, E. Montañés, L.A. Díaz-Secades, 2024, Ocean Engineering.

Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option

José Ramón Quevedo, Oscar Luaces, Antonio Bahamonde, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.