José A. Gámez

发表

Partial evaluation in Rank Aggregation Problems

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2017, Comput. Oper. Res..

Learning heterogeneus cooperative linguistic fuzzy rules using local search: Enhancing the COR search space

José A. Gámez, Javier Cózar, Luis de la Ossa, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

FSS-OBOP: Feature subset selection guided by a bucket order consensus ranking

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2011, Expert Syst. Appl..

Ant Colony and Surrogate Tree-Structured Models for Orderings-Based Bayesian Network Learning

Jose Miguel Puerta, José A. Gámez, John A. W. McCall, 2015, GECCO.

Averaging-Based Ensemble Methods for the Partial Label Ranking Problem

José A. Gámez, Juan A. Aledo, Juan Carlos Alfaro, 2020, HAIS.

Initial breeding value prediction on Manchego sheep by using rule-based systems

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, Expert Syst. Appl..

A Methodology to Evaluate the Effectiveness of Traffic Balancing Algorithms

José A. Gámez, Antonio Robles, José L. Sánchez, 2004, Euro-Par.

Improving model combination through local search in parallel univariate EDAs

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2005, 2005 IEEE Congress on Evolutionary Computation.

Incremental Compilation of Bayesian Networks Based on Maximal Prime Subgraphs

José A. Gámez, Kristian G. Olesen, M. Julia Flores, 2011, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Using metaheuristic algorithms for parameter estimation in generalized Mallows models

José A. Gámez, Juan A. Aledo, David Molina, 2016, Appl. Soft Comput..

Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings

José A. Gámez, Emilio Soria-Olivas, Javier Cózar, 2015, IFSA-EUSFLAT.

Tackling the supervised label ranking problem by bagging weak learners

José A. Gámez, Juan A. Aledo, David Molina, 2017, Inf. Fusion.

GAODE and HAODE: two proposals based on AODE to deal with continuous variables

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2009, ICML '09.

Improved EDNA (estimation of dependency networks algorithm) using combining function with bivariate probability distributions

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2008, GECCO '08.

Adaptive Learning of Probabilistic Graphical Models in Data Mining and Automatic Personalisation TIN2004-06204-C03

José A. Gámez, J. A. Gamez, 2007 .

Adapting the CMIM algorithm for multilabel feature selection. A comparison with existing methods

José A. Gámez, José M. Puerta, Pablo Bermejo, 2018, Expert Syst. J. Knowl. Eng..

Partial abductive inference in Bayesian belief networks - an evolutionary computation approach by using problem-specific genetic operators

José A. Gámez, Luis M. de Campos, Serafín Moral, 2002, IEEE Trans. Evol. Comput..

A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, ECSQARU.

Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2010, IEA/AIE.

Volume, variety and velocity in Data Science

Jose Miguel Puerta, José A. Gámez, Amparo Alonso-Betanzos, 2017, Knowledge-Based Systems.

Special issue on PGM'04: Second European workshop on probabilistic graphical models 2004

José A. Gámez, Peter J. F. Lucas, Antonio Salmerón, 2006, Int. J. Approx. Reason..

Breeding Value Classification in Manchego Sheep: A Study of Attribute Selection and Construction

José A. Gámez, M. Julia Flores, 2005, KES.

The impact of soft computing for the progress of artificial intelligence

José A. Gámez, Antonio Fernández-Caballero, Frank Hoffmann, 2011, Appl. Soft Comput..

TSK-0 Fuzzy Rule-Based Systems for High-Dimensional Problems Using the Apriori Principle for Rule Generation

José A. Gámez, Javier Cózar, Luis de la Ossa, 2014, RSCTC.

Evolutionary and metaheuristics based data mining

María José del Jesús, Jose Miguel Puerta, José A. Gámez, 2009, Soft Comput..

Improvement in the Performance of Island Based Genetic Algorithms Through Path Relinking

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2006, Hybrid Metaheuristics.

Dependency networks based classifiers: learning models by using independence

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2006, Probabilistic Graphical Models.

Partial Abductive Inference in Bayesian Networks By Using Probability Trees

José A. Gámez, Luis M. de Campos, Serafín Moral, 2003, ICEIS.

Use of Explanation Treesto Describe the State Space of a Probabilistic-Based Abduction Problem

José A. Gámez, Serafín Moral, M. Julia Flores, 2008, Innovations in Bayesian Networks.

Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter?

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2011, Applied Intelligence.

Applicability of Estimation of Distribution Algorithms to the Fuzzy Rule Learning Problem: A Preliminary Study

José A. Gámez, M. Julia Flores, 2002, ICEIS.

Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach

José A. Gámez, Javier Cózar, Luis de la Ossa, 2018, Inf. Sci..

One iteration CHC algorithm for learning Bayesian networks: an effective and efficient algorithm for high dimensional problems

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2012, Progress in Artificial Intelligence.

Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers

José A. Gámez, Antonio Salmerón, Ana M. Martínez, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Consensus‐based journal rankings: A complementary tool for bibliometric evaluation

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2018, J. Assoc. Inf. Sci. Technol..

Learning Bayesian Classifiers from Dependency Network Classifiers

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, ICANNGA.

EDNA: Estimation of Dependency Networks Algorithm

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, IWINAC.

Migration of Probability Models Instead of Individuals: An Alternative When Applying the Island Model to EDAs

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2004, PPSN.

Combining Image Invariant Features and Clustering Techniques for Visual Place Classification

José A. Gámez, Jesus Martínez-Gómez, Ismael García-Varea, 2010, ICPR Contests.

Approaching the rank aggregation problem by local search-based metaheuristics

José A. Gámez, Juan A. Aledo, David Molina, 2019, J. Comput. Appl. Math..

On the application of different evolutionary algorithms to the alignment problem in statistical machine translation

José A. Gámez, Ismael García-Varea, Luis Rodríguez, 2008, Neurocomputing.

First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002 - Cuenca (Spain), Electronic Proceedings

José A. Gámez, Antonio Salmerón, 2002, Probabilistic Graphical Models.

Structural Fusion/Aggregation of Bayesian Networks via Greedy Equivalence Search Learning Algorithm

José A. Gámez, Juan A. Aledo, José M. Puerta, 2019, ECSQARU.

Construction of a Semi-Naive Model to Predict Early Readmission of COPD Patients by Using Quality Care Information

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

Low-Complexity Heterogeneous Video Transcoding Using Data Mining

Pedro Cuenca, José A. Gámez, Luis Orozco-Barbosa, 2008, IEEE Transactions on Multimedia.

Fast quadtree level decision algorithm for H.264/HEVC transcoder

Antonio Jesús Díaz-Honrubia, José Luis Martínez, Pedro Cuenca, 2014, 2014 IEEE International Conference on Image Processing (ICIP).

Ant colony optimization for learning Bayesian networks

Jose Miguel Puerta, José A. Gámez, Luis M. de Campos, 2002, Int. J. Approx. Reason..

Improving Revisitation Browsers Capability by Using a Dynamic Bookmarks Personal Toolbar

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2007, WISE.

Generation of first-order TSK rules based on the apriori + search approach

José A. Gámez, Javier Cózar, Luis de la Ossa, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

Random extreme learning machines to predict electric load in buildings

José A. Gámez, Emilio Soria-Olivas, Juan Ignacio Alonso-Barba, 2015, Progress in Artificial Intelligence.

The PDG-Mixture Model for Clustering

José A. Gámez, Jens Dalgaard Nielsen, M. Julia Flores, 2009, DaWaK.

Learning TSK-0 linguistic fuzzy rules by means of local search algorithms

José A. Gámez, Javier Cózar, Luis de la Ossa, 2014, Appl. Soft Comput..

Seleccion genetica para la mejora de la raza ovina manchega mediante tecnicas de Mineria de Datos

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2006, Inteligencia Artif..

Enhancing Incremental Feature Subset Selection in High-Dimensional Databases by Adding a Backward Step

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2011, ISCIS.

Heuristic Based Sampling in Estimation of Distribution Algorithms: An Initial Approach

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2003, CAEPIA.

On the use of local search heuristics to improve GES-based Bayesian network learning

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2018, Appl. Soft Comput..

Scaling Up the Greedy Equivalence Search Algorithm by Constraining the Search Space of Equivalence Classes

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2011, ECSQARU.

Domains of competence of the semi-naive Bayesian network classifiers

José A. Gámez, Ana M. Martínez, M. Julia Flores, 2014, Inf. Sci..

Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs

José A. Gámez, Antonio Salmerón, Jens Dalgaard Nielsen, 2012, Int. J. Approx. Reason..

An Application of Dynamic Bayesian Networks to Condition Monitoring and Fault Prediction in a Sensored System: a Case Study

Jose Miguel Puerta, José A. Gámez, Javier Cózar, 2017, Int. J. Comput. Intell. Syst..

Accelerating chromosome evaluation for partial abductive inference in Bayesian networks by means of explanation set absorption

José A. Gámez, Luis M. de Campos, Serafín Moral, 2001, Int. J. Approx. Reason..

Evaluation of a Thermal-Comfort Control System Using Real Data

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2012, KES.

Searching for the best elimination sequence in Bayesian networks by using ant colony optimization

Jose Miguel Puerta, José A. Gámez, J. M. Puerta, 2002, Pattern Recognit. Lett..

Constrained Score+(Local)Search Methods for Learning Bayesian Networks

Jose Miguel Puerta, José A. Gámez, 2005, ECSQARU.

Utopia in the solution of the Bucket Order Problem

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2017, Decis. Support Syst..

Partial abductive inference in Bayesian belief networks using a genetic algorithm

José A. Gámez, Luis M. de Campos, Serafín Moral, 1999, Pattern Recognit. Lett..

Avoiding premature convergence in estimation of distribution algorithms

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2009, 2009 IEEE Congress on Evolutionary Computation.

Learning weighted linguistic fuzzy rules with estimation of distribution algorithms

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2006, 2006 IEEE International Conference on Evolutionary Computation.

Attribute Construction for E-Mail Foldering by Using Wrappered Forward Greedy Search

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2007, ICEIS.

A Pairwise Class Interaction Framework for Multilabel Classification

Jose Miguel Puerta, José A. Gámez, Thomas D. Nielsen, 2014, Probabilistic Graphical Models.

CiDAEN: An Online Data Science Course

Jose Miguel Puerta, José A. Gámez, Juan A. Aledo, 2019, HELMeTO.

A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity

Francisco Herrera, José A. Gámez, Alberto Fernández, 2019, IEEE Transactions on Fuzzy Systems.

Partial abductive inference in Bayesian belief networks by simulated annealing

José A. Gámez, Luis M. de Campos, Serafín Moral, 2001, Int. J. Approx. Reason..

Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade‐off between Efficiency and Accuracy

Jose Miguel Puerta, José A. Gámez, Jacinto Arias, 2015, Int. J. Intell. Syst..

Guest editorial: new trends in probabilistic graphical models

José A. Gámez, Antonio Salmerón, 2004 .

Using Genetic Algorithms for Real-Time Object Detection

José A. Gámez, Vicente Matellán Olivera, Jesus Martínez-Gómez, 2009, RoboCup.

Single- and Multi-label Prediction of Burden on Families of Schizophrenia Patients

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2013, AIME.

Tackling the rank aggregation problem with evolutionary algorithms

José A. Gámez, Juan A. Aledo, David Molina, 2013, Appl. Math. Comput..

The Independency tree model: a new approach for clustering and factorisation

José A. Gámez, Serafín Moral, M. Julia Flores, 2006, Probabilistic Graphical Models.

Abductive Inference in Bayesian Networks: A Review

José A. Gámez, J. A. Gamez, 2004 .

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2012, Knowl. Based Syst..

Simplifying Explanations in Bayesian Belief Networks

José A. Gámez, Luis M. de Campos, Serafín Moral, 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Improving Incremental Wrapper-Based Feature Subset Selection by Using Re-ranking

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2010, IEA/AIE.

Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

José A. Gámez, Rafael Rumí, Antonio Salmerón, 2006, Probabilistic Graphical Models.

Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2011, Int. J. Approx. Reason..

Graphical Models to Causal Discovery from Data

Jose Miguel Puerta, José A. Gámez, Luis M. de Campos, 2002, Probabilistic Graphical Models.

Comparing ELM Against MLP for Electrical Power Prediction in Buildings

José A. Gámez, Emilio Soria-Olivas, Javier Cózar, 2015, IWINAC.

Searching for alignments in SMT. A novel approach based on an Estimation of Distribution Algorithm

José A. Gámez, Ismael García-Varea, Luis Rodríguez, 2006, WMT@HLT-NAACL.

Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

Scalable Learning of k-dependence Bayesian Classifiers under MapReduce

Jose Miguel Puerta, José A. Gámez, Jacinto Arias, 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

Comparison of balancing techniques for multimedia IR over imbalanced datasets

Jose Miguel Puerta, José A. Gámez, Frank Hopfgartner, 2009, 2009 24th International Symposium on Computer and Information Sciences.

Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood

Jose Miguel Puerta, José A. Gámez, Juan L. Mateo, 2010, Data Mining and Knowledge Discovery.

A scalable pairwise class interaction framework for multidimensional classification

Jose Miguel Puerta, José A. Gámez, Thomas D. Nielsen, 2016, Int. J. Approx. Reason..

Gait Optimization in AIBO Robots Using an Estimation of Distribution Algorithm

Jose Miguel Puerta, José A. Gámez, Juan Ignacio Alonso-Barba, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

Building efficient fuzzy regression trees for large scale and high dimensional problems

José A. Gámez, Francesco Marcelloni, Javier Cózar, 2018, Journal of Big Data.

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers

José A. Gámez, Geoffrey I. Webb, Ana M. Martínez, 2012, HAIS.

Learning distributed discrete Bayesian Network Classifiers under MapReduce with Apache Spark

Jose Miguel Puerta, José A. Gámez, Jacinto Arias, 2017, Knowl. Based Syst..

Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space

José A. Gámez, M. Julia Flores, Serafín Moral, 2005, ECSQARU.

Initial Approaches to the Application of Islands-Based Parallel EDAs in Continuous Domains

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2005, ICPP Workshops.

Speeding up incremental wrapper feature subset selection with Naive Bayes classifier

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2014, Knowl. Based Syst..

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data

José A. Gámez, Enrique G. Rodrigo, Juan A. Aledo, 2019, J. Mach. Learn. Res..

A study on different backward feature selection criteria over high-dimensional databases

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

On the discovery of association rules by means of evolutionary algorithms

María José del Jesús, Jose Miguel Puerta, José A. Gámez, 2011, WIREs Data Mining Knowl. Discov..

Initial approaches to the application of islands-based parallel EDAs in continuous domains

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

HODE: Hidden One-Dependence Estimator

Jose Miguel Puerta, José A. Gámez, Ana M. Martínez, 2009, ECSQARU.

A Probabilistic Graphical Model-Based Approach for the Label Ranking Problem

José A. Gámez, Enrique G. Rodrigo, Juan A. Aledo, 2019, ECSQARU.

Probabilistic graphical models

José A. Gámez, Antonio Salmerón, A. Salmerón, 2003, Int. J. Intell. Syst..

Improving Incremental Wrapper-Based Subset Selection via Replacement and Early Stopping

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2011, Int. J. Pattern Recognit. Artif. Intell..

Incremental compilation of Bayesian networks

José A. Gámez, Kristian G. Olesen, M. Julia Flores, 2002, UAI.

A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination

José A. Gámez, Antonio Salmerón, Jens Dalgaard Nielsen, 2014, Int. J. Approx. Reason..

Learning cooperative linguistic fuzzy rules using fast local search algorithms

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2010, International Conference on Fuzzy Systems.

Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms

Jose Miguel Puerta, José A. Gámez, Luis de la Ossa, 2009, Int. J. Approx. Reason..

Bayesian Network Classifiers Under the Ensemble Perspective

Jose Miguel Puerta, José A. Gámez, Jacinto Arias, 2018, PGM.

An improved Markov-based localization approach by using image quality evaluation

José A. Gámez, Jesus Martínez-Gómez, Ismael García-Varea, 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

Data clustering using hidden variables in hybrid Bayesian networks

José A. Gámez, Rafael Rumí, Antonio Salmerón, 2014, Progress in Artificial Intelligence.

Learning more Accurate Bayesian Networks in the CHC Approach by Adjusting the Trade-Off between Efficiency and Accuracy

Jose Miguel Puerta, José A. Gámez, Jacinto Arias, 2013, CAEPIA.

Medical images modality classification using discrete Bayesian Networks

José A. Gámez, Henning Müller, Alba Garcia Seco de Herrera, 2016 .

Obtaining the optimal configuration of high-radix Combined switches

José A. Gámez, José Duato, Francisco J. Andujar, 2013, J. Parallel Distributed Comput..

Using extension sets to aggregate partial rankings in a flexible setting

José A. Gámez, Juan A. Aledo, David Molina, 2016, Appl. Math. Comput..

Proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 9422

José A. Gámez, Mikel Galar, Bernabé Dorronsoro, 2015 .

Guest Editorial: Recent Trends in Intelligent Systems

Francisco Herrera, Jose Miguel Puerta, José A. Gámez, 2017, Int. J. Intell. Syst..

Improvement of a car racing controller by means of Ant Colony Optimization algorithms

José A. Gámez, Luis de la Ossa, Verónica López, 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets

José A. Gámez, M. Julia Flores, 2015, ICAART.

Triangulation of Bayesian networks by retriangulation

José A. Gámez, M. Julia Flores, 2003, Int. J. Intell. Syst..

Advances in Bayesian networks

José A. Gámez, Serafín Moral, Antonio Salmerón, 2004 .

Economic modelling of robotic disassembly in end-of-life product recovery for remanufacturing

José A. Gámez, Juan A. Aledo, Duc Truong Pham, 2020, Comput. Ind. Eng..

Machine learning from crowds: A systematic review of its applications

José A. Gámez, Enrique G. Rodrigo, Juan A. Aledo, 2018, WIREs Data Mining Knowl. Discov..

Computing the Consensus Permutation in Mallows Distribution by Using Genetic Algorithms

José A. Gámez, Juan A. Aledo, David Molina, 2013, IEA/AIE.

Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2018, Eur. J. Oper. Res..

Comparing Cellular and Panmictic Genetic Algorithms for Real-Time Object Detection

José A. Gámez, Jesus Martínez-Gómez, Ismael García-Varea, 2010, EvoApplications.

A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets

Jose Miguel Puerta, José A. Gámez, Pablo Bermejo, 2011, Pattern Recognit. Lett..

OC1-DE: A Differential Evolution Based Approach for Inducing Oblique Decision Trees

Jose Miguel Puerta, José A. Gámez, Rafael Rivera-López, 2017, ICAISC.

A highly scalable algorithm for weak rankings aggregation

José A. Gámez, Juan A. Aledo, Alejandro Rosete, 2021, Inf. Sci..

STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines

Jose Miguel Puerta, José A. Gámez, Ricardo Montañana, 2021, CAEPIA.

Mixture-Based Probabilistic Graphical Models for the Partial Label Ranking Problem

José A. Gámez, Juan A. Aledo, Juan C. Alfaro, 2021, IDEAL.

A Data-Driven Approach for Components Useful Life Estimation in Wind Turbines

José A. Gámez, Jesus Martínez-Gómez, Alejandro Zornoza Martínez, 2021, SOCO.

Improving KNN-based e-mail classification into folders generating class-balanced datasets.

José A. Gámez, José M. Puerta, Pablo Bermejo, 2008 .

A Review on Distinct Methods and Approaches to Perform Triangulation for Bayesian Networks

José A. Gámez, M. Julia Flores, M. Flores, 2007 .

Predicción del valor genético en ovejas de raza manchega usando técnicas de aprendizaje automático

José A. Gámez, Antonio Salmerón, 2005 .

SUPPORTING INTELLIGENT SYSTEMS TEACHING BY MEANS OF COMPUTER GAMES PROGRAMMING

José A. Gámez, José M. Puerta, J. Alonso, 2010 .