 | 2009 |
| 45 |  | Pablo Bermejo,
José A. Gámez,
Jose Miguel Puerta:
Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection.
CIDM 2009: 367-374 |
| 44 |  | M. Julia Flores,
José A. Gámez,
Jens D. Nielsen:
The PDG-Mixture Model for Clustering.
DaWaK 2009: 378-389 |
| 43 |  | M. Julia Flores,
José A. Gámez,
Ana M. Martínez,
Jose Miguel Puerta:
HODE: Hidden One-Dependence Estimator.
ECSQARU 2009: 481-492 |
| 42 |  | M. Julia Flores,
José A. Gámez,
Ana M. Martínez,
Jose Miguel Puerta:
GAODE and HAODE: two proposals based on AODE to deal with continuous variables.
ICML 2009: 40 |
| 41 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms.
Int. J. Approx. Reasoning 50(3): 541-560 (2009) |
| 40 |  | María J. del Jesús,
José A. Gámez,
Jose Miguel Puerta:
Evolutionary and metaheuristics based data mining.
Soft Comput. 13(3): 209-212 (2009) |
| 2008 |
| 39 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
Improved EDNA (estimation of dependency networks algorithm) using combining function with bivariate probability distributions.
GECCO 2008: 407-414 |
| 38 |  | José A. Gámez,
Ismael García-Varea,
Jesus Martinez-Gomez:
An improved Markov-based localization approach by using image quality evaluation.
ICARCV 2008: 1236-1241 |
| 37 |  | M. Julia Flores,
José A. Gámez,
Serafín Moral:
Use of Explanation Treesto Describe the State Space of a Probabilistic-Based Abduction Problem.
Innovations in Bayesian Networks 2008: 251-280 |
| 36 |  | Gerardo Fernández-Escribano,
Jens Bialkowski,
José A. Gámez,
Hari Kalva,
Pedro Cuenca,
Luis Orozco-Barbosa,
André Kaup:
Low-Complexity Heterogeneous Video Transcoding Using Data Mining.
IEEE Transactions on Multimedia 10(2): 286-299 (2008) |
| 35 |  | Luis Rodríguez,
Ismael García-Varea,
José A. Gámez:
On the application of different evolutionary algorithms to the alignment problem in statistical machine translation.
Neurocomputing 71(4-6): 755-765 (2008) |
| 2007 |
| 34 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
A Fast Hill-Climbing Algorithm for Bayesian Networks Structure Learning.
ECSQARU 2007: 585-597 |
| 33 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
Learning Bayesian Classifiers from Dependency Network Classifiers.
ICANNGA (1) 2007: 806-813 |
| 32 |  | Pablo Bermejo,
José A. Gámez,
Jose Miguel Puerta:
Attribute Construction for E-Mail Foldering by Using Wrappered Forward Greedy Search.
ICEIS (2) 2007: 247-252 |
| 31 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
EDNA: Estimation of Dependency Networks Algorithm.
IWINAC (1) 2007: 427-436 |
| 30 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
Improving Revisitation Browsers Capability by Using a Dynamic Bookmarks Personal Toolbar.
WISE 2007: 643-652 |
| 29 |  | Luis de la Ossa,
M. Julia Flores,
José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
Initial breeding value prediction on Manchego sheep by using rule-based systems.
Expert Syst. Appl. 33(1): 96-109 (2007) |
| 2006 |
| 28 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Improvement in the Performance of Island Based Genetic Algorithms Through Path Relinking.
Hybrid Metaheuristics 2006: 42-56 |
| 27 |  | José A. Gámez,
Juan L. Mateo,
Jose Miguel Puerta:
Dependency networks based classifiers: learning models by using independence.
Probabilistic Graphical Models 2006: 115-122 |
| 26 |  | José A. Gámez,
Rafael Rumí,
Antonio Salmerón:
Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials.
Probabilistic Graphical Models 2006: 123-130 |
| 25 |  | M. Julia Flores,
José A. Gámez,
Serafín Moral:
The Independency tree model: a new approach for clustering and factorisation.
Probabilistic Graphical Models 2006: 83-90 |
| 24 |  | Peter J. F. Lucas,
José A. Gámez,
Antonio Salmerón:
Special issue on PGM'04: Second European workshop on probabilistic graphical models 2004.
Int. J. Approx. Reasoning 42(1-2): 1-3 (2006) |
| 23 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Initial approaches to the application of islands-based parallel EDAs in continuous domains.
J. Parallel Distrib. Comput. 66(8): 991-1001 (2006) |
| 2005 |
| 22 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Improving model combination through local search in parallel univariate EDAs.
Congress on Evolutionary Computation 2005: 1426-1433 |
| 21 |  | José A. Gámez,
Jose Miguel Puerta:
Constrained Score+(Local)Search Methods for Learning Bayesian Networks.
ECSQARU 2005: 161-173 |
| 20 |  | M. Julia Flores,
José A. Gámez,
Serafín Moral:
Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space.
ECSQARU 2005: 63-75 |
| 19 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Initial Approaches to the Application of Islands-Based Parallel EDAs in Continuous Domains.
ICPP Workshops 2005: 580-587 |
| 18 |  | M. Julia Flores,
José A. Gámez:
Breeding Value Classification in Manchego Sheep: A Study of Attribute Selection and Construction.
KES (2) 2005: 1338-1346 |
| 2004 |
| 17 |  | J. E. Villalobos,
J. L. Sánchez,
José A. Gámez,
José Carlos Sancho,
Antonio Robles:
A Methodology to Evaluate the Effectiveness of Traffic Balancing Algorithms.
Euro-Par 2004: 891-899 |
| 16 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Migration of Probability Models Instead of Individuals: An Alternative When Applying the Island Model to EDAs.
PPSN 2004: 242-252 |
| 2003 |
| 15 |  | Luis de la Ossa,
José A. Gámez,
Jose Miguel Puerta:
Heuristic Based Sampling in Estimation of Distribution Algorithms: An Initial Approach.
CAEPIA 2003: 384-393 |
| 14 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Partial Abductive Inference in Bayesian Networks By Using Probability Trees.
ICEIS (2) 2003: 83-91 |
| 13 |  | M. Julia Flores,
José A. Gámez,
Kristian G. Olesen:
Incremental compilation of Bayesian networks.
UAI 2003: 233-240 |
| 12 |  | José A. Gámez,
Antonio Salmerón:
Probabilistic graphical models.
Int. J. Intell. Syst. 18(2): 149-151 (2003) |
| 11 |  | M. Julia Flores,
José A. Gámez:
Triangulation of Bayesian networks by retriangulation.
Int. J. Intell. Syst. 18(2): 153-164 (2003) |
| 2002 |
| 10 |  | José A. Gámez,
Antonio Salmerón:
First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002 - Cuenca (Spain), Electronic Proceedings
Probabilistic Graphical Models 2002 |
| 9 |  | M. Julia Flores,
José A. Gámez:
Applicability of Estimation of Distribution Algorithms to the Fuzzy Rule Learning Problem: A Preliminary Study.
ICEIS 2002: 350-357 |
| 8 |  | Luis M. de Campos,
José A. Gámez,
Jose Miguel Puerta:
Graphical Models to Causal Discovery from Data.
Probabilistic Graphical Models 2002 |
| 7 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Partial abductive inference in Bayesian belief networks - an evolutionary computation approach by using problem-specific genetic operators.
IEEE Trans. Evolutionary Computation 6(2): 105-131 (2002) |
| 6 |  | Luis M. de Campos,
Juan M. Fernández-Luna,
José A. Gámez,
Jose Miguel Puerta:
Ant colony optimization for learning Bayesian networks.
Int. J. Approx. Reasoning 31(3): 291-311 (2002) |
| 5 |  | José A. Gámez,
Jose Miguel Puerta:
Searching for the best elimination sequence in Bayesian networks by using ant colony optimization.
Pattern Recognition Letters 23(1-3): 261-277 (2002) |
| 2001 |
| 4 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Accelerating chromosome evaluation for partial abductive inference in Bayesian networks by means of explanation set absorption.
Int. J. Approx. Reasoning 27(2): 121-142 (2001) |
| 3 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Partial abductive inference in Bayesian belief networks by simulated annealing.
Int. J. Approx. Reasoning 27(3): 263-283 (2001) |
| 2 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Simplifying Explanations in Bayesian Belief Networks.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(4): 461-490 (2001) |
| 1999 |
| 1 |  | Luis M. de Campos,
José A. Gámez,
Serafín Moral:
Partial abductive inference in Bayesian belief networks using a genetic algorithm.
Pattern Recognition Letters 20(11-13): 1211-1217 (1999) |