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José María Valls: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. José María Valls, Inés María Galván, Pedro Isasi
    How the Selection of Training Patterns can Improve the Generalization Capability in Radial Basis Neural Networks. [Citation Graph (0, 0)][DBLP]
    Applied Informatics, 2003, pp:275-280 [Conf]
  2. César Estébanez, José María Valls, Ricardo Aler
    Projecting Financial Data Using Genetic Programming in Classification and Regression Tasks. [Citation Graph (0, 0)][DBLP]
    EuroGP, 2006, pp:202-212 [Conf]
  3. César Estébanez, José María Valls, Ricardo Aler, Inés María Galván
    A First Attempt at Constructing Genetic Programming Expressions for EEG Classification. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2005, pp:665-670 [Conf]
  4. José María Valls, Inés María Galván, Pedro Isasi
    Lazy Training of Radial Basis Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:198-207 [Conf]
  5. José María Valls, Pedro Isasi, Inés María Galván
    Deferring the Learning for Better Generalization in Radial Basis Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:189-195 [Conf]
  6. Pedro Isasi, José María Valls, Inés María Galván
    A Better Selection of Patterns in Lazy Learning Radial Basis Neural Networks. [Citation Graph (0, 0)][DBLP]
    IWANN (1), 2003, pp:278-285 [Conf]
  7. José María Valls, Ricardo Aler, Oscar Fernández
    Using a Mahalanobis-Like Distance to Train Radial Basis Neural Networks. [Citation Graph (0, 0)][DBLP]
    IWANN, 2005, pp:257-263 [Conf]
  8. César Estébanez, Ricardo Aler, José María Valls
    Genetic Programming Based Data Projections for Classification Tasks. [Citation Graph (0, 0)][DBLP]
    IEC (Prague), 2005, pp:56-61 [Conf]
  9. José María Valls, José M. Molina, Inés María Galván
    Sistema Multiagente para el diseño de Redes de Neuronas de Base Radial Óptimas. [Citation Graph (0, 0)][DBLP]
    Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 2000, v:10, n:, pp:18-25 [Journal]
  10. José M. Molina, Inés María Galván, José María Valls, Andrés Leal
    Optimizing the Number of Learning Cycles in the Design of Radial Basis Neural Networks Using a Multi-Agent System. [Citation Graph (0, 0)][DBLP]
    Computers and Artificial Intelligence, 2001, v:20, n:5, pp:- [Journal]
  11. José María Valls, Inés María Galván, Pedro Isasi Viñuela
    Improving the Generalization Ability of RBNN Using a Selective Strategy Based on the Gaussian Kernel Function. [Citation Graph (0, 0)][DBLP]
    Computers and Artificial Intelligence, 2006, v:25, n:1, pp:- [Journal]
  12. Inés María Galván, Pedro Isasi, Ricardo Aler, José María Valls
    A Selective Learning Method to Improve the Generalization of Multilayer Feedforward Neural Networks. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 2001, v:11, n:2, pp:167-177 [Journal]
  13. José María Valls, Inés María Galván, Pedro Isasi
    Lazy Learning in Radial Basis Neural Networks: A Way of Achieving More Accurate Models. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2004, v:20, n:2, pp:105-124 [Journal]
  14. Cristóbal Luque del Arco-Calderón, José María Valls, Pedro Isasi Viñuela
    Time Series Forecasting by means of Evolutionary Algorithms. [Citation Graph (0, 0)][DBLP]
    IPDPS, 2007, pp:1-7 [Conf]
  15. Ricardo Aler, Oscar Garcia, José María Valls
    Correcting and improving imitation models of humans for Robosoccer agents. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:2402-2409 [Conf]
  16. José María Valls, Inés María Galván, Pedro Isasi
    LRBNN: A Lazy Radial Basis Neural Network model. [Citation Graph (0, 0)][DBLP]
    AI Commun., 2007, v:20, n:2, pp:71-86 [Journal]
  17. José María Valls, Ricardo Aler, Oscar Fernández
    Evolving Generalized Euclidean Distances for Training RBNN. [Citation Graph (0, 0)][DBLP]
    Computers and Artificial Intelligence, 2007, v:26, n:1, pp:- [Journal]
  18. César Estébanez, Ricardo Aler, José María Valls
    A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems. [Citation Graph (0, 0)][DBLP]
    IJCSA, 2007, v:4, n:1, pp:69-80 [Journal]

  19. An Experimental Study on Fitness Distributions of Tree Shapes in GP with One-Point Crossover. [Citation Graph (, )][DBLP]


  20. Metaheuristics for solving a real-world frequency assignment problem in GSM networks. [Citation Graph (, )][DBLP]


  21. Optimizing Data Transformations for Classification Tasks. [Citation Graph (, )][DBLP]


  22. Optimizing Linear and Quadratic Data Transformations for Classification Tasks. [Citation Graph (, )][DBLP]


  23. Two-layered evolutionary forecasting for IPO underpricing. [Citation Graph (, )][DBLP]


  24. Improving Classification for Brain Computer Interfaces using Transitions and a Moving Window. [Citation Graph (, )][DBLP]


  25. GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains. [Citation Graph (, )][DBLP]


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