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Ricardo Bastos Cavalcante Prudêncio: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    Using Machine Learning Techniques to Combine Forecasting Methods. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:1122-1127 [Conf]
  2. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    Selection of Models for Time Series Prediction via Meta-Learning. [Citation Graph (0, 0)][DBLP]
    HIS, 2002, pp:74-83 [Conf]
  3. Patrícia Maforte dos Santos, Teresa Bernarda Ludermir, Ricardo Bastos Cavalcante Prudêncio
    Selection of Time Series Forecasting Models based on Performance Information. [Citation Graph (0, 0)][DBLP]
    HIS, 2004, pp:366-371 [Conf]
  4. Eduardo F. A. Silva, Flávia A. Barros, Ricardo Bastos Cavalcante Prudêncio
    A Hybrid Machine Learning Approach for Information Extraction. [Citation Graph (0, 0)][DBLP]
    HIS, 2006, pp:44- [Conf]
  5. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    Selecting and Ranking Time Series Models Using the NOEMON Approach. [Citation Graph (0, 0)][DBLP]
    ICANN, 2003, pp:654-661 [Conf]
  6. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    A Machine Learning Approach to Define Weights for Linear Combination of Forecasts. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:274-283 [Conf]
  7. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    Meta-learning approaches to selecting time series models. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2004, v:61, n:, pp:121-137 [Journal]
  8. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho
    A Modal Symbolic Classifier for selecting time series models. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2004, v:25, n:8, pp:911-921 [Journal]
  9. Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
    Active Learning to Support the Generation of Meta-examples. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2007, pp:817-826 [Conf]

  10. Active Selection of Training Examples for Meta-Learning. [Citation Graph (, )][DBLP]


  11. Hidden Markov Models and Text Classifiers for Information Extraction on Semi-Structured Texts. [Citation Graph (, )][DBLP]


  12. Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors. [Citation Graph (, )][DBLP]


  13. Active Generation of Training Examples in Meta-Regression. [Citation Graph (, )][DBLP]


  14. Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data. [Citation Graph (, )][DBLP]


  15. Local Feature Selection in Text Clustering. [Citation Graph (, )][DBLP]


  16. Ranking and selecting clustering algorithms using a meta-learning approach. [Citation Graph (, )][DBLP]


  17. Active Meta-Learning with Uncertainty Sampling and Outlier Detection. [Citation Graph (, )][DBLP]


  18. Combining Uncertainty Sampling Methods for Active Meta-Learning. [Citation Graph (, )][DBLP]


  19. Automatic Information Extraction in Semi-structured Official Journals. [Citation Graph (, )][DBLP]


  20. Selecting Neural Network Forecasting Models Using the Zoomed-Ranking Approach. [Citation Graph (, )][DBLP]


  21. Using Support Vector Machines to Predict the Performance of MLP Neural Networks. [Citation Graph (, )][DBLP]


  22. LearningWeights for Linear Combination of Forecasting Methods. [Citation Graph (, )][DBLP]


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