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José Manuel Peña: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. José Manuel Peña, Víctor Robles, Oscar Marbán, María S. Pérez
    Bayesian Methods to Estimate Future Load in Web Farms. [Citation Graph (0, 0)][DBLP]
    AWIC, 2004, pp:217-226 [Conf]
  2. María S. Pérez, Alberto Sánchez, Pilar Herrero, Víctor Robles, José Manuel Peña
    Adapting the Weka Data Mining Toolkit to a Grid Based Environment. [Citation Graph (0, 0)][DBLP]
    AWIC, 2005, pp:492-497 [Conf]
  3. María S. Pérez, Alberto Sánchez, Víctor Robles, José Manuel Peña, Jemal H. Abawajy
    Cooperation model of a multiagent parallel file system for clusters. [Citation Graph (0, 0)][DBLP]
    CCGRID, 2004, pp:595-601 [Conf]
  4. José Manuel Peña, Sylvain Létourneau, Fazel Famili
    Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure. [Citation Graph (0, 0)][DBLP]
    IDA, 1999, pp:473-486 [Conf]
  5. José Manuel Peña, Fazel Famili, Sylvain Létourneau
    Data mining to detect abnormal behavior in aerospace data. [Citation Graph (0, 0)][DBLP]
    KDD, 2000, pp:390-397 [Conf]
  6. María S. Pérez, Alberto Sánchez, José Manuel Peña, Víctor Robles, Jesús Carretero, Félix García
    Storage groups: A new approach for providing dynamic reconfiguration in data-based clusters. [Citation Graph (0, 0)][DBLP]
    Parallel and Distributed Computing and Networks, 2004, pp:70-75 [Conf]
  7. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. [Citation Graph (0, 0)][DBLP]
    Probabilistic Graphical Models, 2002, pp:- [Conf]
  8. Alberto Sánchez, María S. Pérez, Víctor Robles, José Manuel Peña, Pilar Herrero
    A Flexible Two-Level I/O Architecture for Grids. [Citation Graph (0, 0)][DBLP]
    SAG, 2004, pp:50-58 [Conf]
  9. Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José Manuel Peña
    Combinatonal Optimization by Learning and Simulation of Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:343-352 [Conf]
  10. Jens D. Nielsen, Tomás Kocka, José Manuel Peña
    On Local Optima in Learning Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:435-442 [Conf]
  11. Víctor Robles, Pedro Larrañaga, José Manuel Peña, Ernestina Menasalvas Ruiz, María S. Pérez, Vanessa Herves, Anita Wasilewska
    Bayesian network multi-classifiers for protein secondary structure prediction. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2004, v:31, n:2, pp:117-136 [Journal]
  12. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    Evolutionary Computation, 2005, v:13, n:1, pp:43-66 [Journal]
  13. María S. Pérez, Jesús Carretero, Félix García Carballeira, José Manuel Peña, Víctor Robles
    MAPFS: A flexible multiagent parallel file system for clusters. [Citation Graph (0, 0)][DBLP]
    Future Generation Comp. Syst., 2006, v:22, n:5, pp:620-632 [Journal]
  14. María S. Pérez, Alberto Sánchez, Víctor Robles, Pilar Herrero, José Manuel Peña
    Design and implementation of a data mining grid-aware architecture. [Citation Graph (0, 0)][DBLP]
    Future Generation Comp. Syst., 2007, v:23, n:1, pp:42-47 [Journal]
  15. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Performance evaluation of compromise conditional Gaussian networks for data clustering. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2001, v:28, n:1, pp:23-50 [Journal]
  16. Ernestina Menasalvas Ruiz, Socorro Millán, José Manuel Peña, Michael Hadjimichael, Oscar Marbán
    Subsessions: A granular approach to click path analysis. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2004, v:19, n:7, pp:619-637 [Journal]
  17. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. [Citation Graph (0, 0)][DBLP]
    International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, v:12, n:Supplement-1, pp:63-82 [Journal]
  18. María S. Pérez, Alberto Sánchez, José Manuel Peña, Víctor Robles
    A new formalism for dynamic reconfiguration of data servers in a cluster. [Citation Graph (0, 0)][DBLP]
    J. Parallel Distrib. Comput., 2005, v:65, n:10, pp:1134-1145 [Journal]
  19. Pedro Larrañaga, José Antonio Lozano, José Manuel Peña, Iñaki Inza
    Editorial. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:59, n:3, pp:211-212 [Journal]
  20. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:47, n:1, pp:63-89 [Journal]
  21. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga, Iñaki Inza
    Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2001, v:23, n:6, pp:590-603 [Journal]
  22. Iñaki Inza, Pedro Larrañaga, Basilio Sierra, Ramon Etxeberria, José Antonio Lozano, José Manuel Peña
    Representing the behaviour of supervised classification learning algorithms by Bayesian networks. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 1999, v:20, n:11-13, pp:1201-1209 [Journal]
  23. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2000, v:21, n:8, pp:779-786 [Journal]
  24. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    An empirical comparison of four initialization methods for the K-Means algorithm. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 1999, v:20, n:10, pp:1027-1040 [Journal]
  25. José Manuel Peña, José Antonio Lozano, Pedro Larrañaga
    Learning Bayesian networks for clustering by means of constructive induction. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 1999, v:20, n:11-13, pp:1219-1230 [Journal]

  26. Using multiple offspring sampling to guide genetic algorithms to solve permutation problems. [Citation Graph (, )][DBLP]


  27. Voronoi-initializated island models for solving real-coded deceptive problems. [Citation Graph (, )][DBLP]


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