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Francisco de A. T. de Carvalho: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Nicomedes Cavalcanti, Francisco de A. T. de Carvalho
    An Adaptive Fuzzy c-Means Algorithm with the L2 Norm. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2005, pp:1138-1141 [Conf]
  2. Byron L. D. Bezerra, Francisco de A. T. de Carvalho
    A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:1011-1016 [Conf]
  3. Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Camilo P. Tenorio
    Univariate and Multivariate Linear Regression Methods to Predict Interval-Valued Features. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:526-537 [Conf]
  4. Fabrice Rossi, Francisco de A. T. de Carvalho, Yves Lechevallier, Alzennyr Da Silva
    Comparaison de dissimilarité pour l'analyse de l'usage d'un site web. [Citation Graph (0, 0)][DBLP]
    EGC, 2006, pp:409-414 [Conf]
  5. Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho
    Construction et analyse de résumés de données évolutives : application aux données d'usage du Web. [Citation Graph (0, 0)][DBLP]
    EGC, 2007, pp:539-544 [Conf]
  6. Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Gustavo Alves
    Collaborative Filtering Based on Modal Symbolic User Profiles: Knowing You in the First Meeting. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2004, pp:235-245 [Conf]
  7. Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza
    A Classifier for Quantitative Feature Values Based on a Region Oriented Symbolic Approach. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2004, pp:464-473 [Conf]
  8. Alzennyr Da Silva, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir, Nicomedes Cavalcanti
    Comparing Metrics in Fuzzy Clustering for Symbolic Data on SODAS Format. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2004, pp:727-736 [Conf]
  9. Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Camilo P. Tenorio
    Two Partitional Methods for Interval-Valued Data Using Mahalanobis Distances. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2004, pp:454-463 [Conf]
  10. Francisco de A. T. de Carvalho
    A Fuzzy Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Euclidean Distance. [Citation Graph (0, 0)][DBLP]
    ICONIP (3), 2006, pp:1012-1021 [Conf]
  11. Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza
    Classification of SAR Images Through a Convex Hull Region Oriented Approach. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:769-774 [Conf]
  12. André Luis S. Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir
    A Hybrid Model for Symbolic Interval Time Series Forecasting. [Citation Graph (0, 0)][DBLP]
    ICONIP (2), 2006, pp:934-941 [Conf]
  13. Fabio C. D. Silva, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Joyce Q. Silva
    A Modal Symbolic Classifier for Interval Data. [Citation Graph (0, 0)][DBLP]
    ICONIP (2), 2006, pp:50-59 [Conf]
  14. Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Fabio C. D. Silva
    Clustering of Interval-Valued Data Using Adaptive Squared Euclidean Distances. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:775-780 [Conf]
  15. Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho, Brigitte Trousse
    Mining Web Usage Data for Discovering Navigation Clusters. [Citation Graph (0, 0)][DBLP]
    ISCC, 2006, pp:910-915 [Conf]
  16. Francisco de A. T. de Carvalho, Eufrasio de A. Lima Neto, Camilo P. Tenorio
    A New Method to Fit a Linear Regression Model for Interval-Valued Data. [Citation Graph (0, 0)][DBLP]
    KI, 2004, pp:295-306 [Conf]
  17. Eufrasio de A. Lima Neto, Francisco de A. T. de Carvalho, Eduarda S. Freire
    Applying Constrained Linear Regression Models to Predict Interval-Valued Data. [Citation Graph (0, 0)][DBLP]
    KI, 2005, pp:92-106 [Conf]
  18. Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Geber Ramalho, Jean-Daniel Zucker
    Speeding up Recommender Systems with Meta-prototypes. [Citation Graph (0, 0)][DBLP]
    SBIA, 2002, pp:227-236 [Conf]
  19. Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Fabio C. D. Silva
    A Clustering Method for Symbolic Interval-Type Data Using Adaptive Chebyshev Distances. [Citation Graph (0, 0)][DBLP]
    SBIA, 2004, pp:266-275 [Conf]
  20. Sérgio R. de M. Queiroz, Francisco de A. T. de Carvalho
    Making Collaborative Group Recommendations Based on Modal Symbolic Data. [Citation Graph (0, 0)][DBLP]
    SBIA, 2004, pp:307-316 [Conf]
  21. Sérgio R. de M. Queiroz, Francisco de A. T. de Carvalho, Geber Ramalho, Vincent Corruble
    Making Recommendations for Groups Using Collaborative Filtering and Fuzzy Majority. [Citation Graph (0, 0)][DBLP]
    SBIA, 2002, pp:248-258 [Conf]
  22. Ivan R. Teixeira, Francisco de A. T. de Carvalho, Geber Ramalho, Vincent Corruble
    ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems. [Citation Graph (0, 0)][DBLP]
    SBIA, 2002, pp:237-247 [Conf]
  23. Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto
    A Symbolic Approach to Gene Expression Time Series Analysis. [Citation Graph (0, 0)][DBLP]
    SBRN, 2002, pp:25-30 [Conf]
  24. Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Valmir Macario Filho
    C^2: : A Collaborative Recommendation System Based on Modal Symbolic User Profile. [Citation Graph (0, 0)][DBLP]
    Web Intelligence, 2006, pp:673-679 [Conf]
  25. Luciano Barbosa, Ana Carolina Salgado, Francisco de A. T. de Carvalho, Jacques Robin, Juliana Freire
    Looking at both the present and the past to efficiently update replicas of web content. [Citation Graph (0, 0)][DBLP]
    WIDM, 2005, pp:75-80 [Conf]
  26. Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto
    Stability Evaluation of Clustering Algorithms for Time Series Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    WOB, 2002, pp:88-90 [Conf]
  27. Francisco de A. T. de Carvalho, Camilo P. Tenorio, Nicomedes L. Cavalcanti Junior
    Partitional fuzzy clustering methods based on adaptive quadratic distances. [Citation Graph (0, 0)][DBLP]
    Fuzzy Sets and Systems, 2006, v:157, n:21, pp:2833-2857 [Journal]
  28. Byron L. D. Bezerra, Francisco de A. T. de Carvalho
    A symbolic approach for content-based information filtering. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 2004, v:92, n:1, pp:45-52 [Journal]
  29. Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto
    Comparative study on proximity indices for cluster analysis of gene expression time series. [Citation Graph (0, 0)][DBLP]
    Journal of Intelligent and Fuzzy Systems, 2002, v:13, n:2-4, pp:133-142 [Journal]
  30. Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Marie Chavent, Yves Lechevallier
    Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2006, v:27, n:3, pp:167-179 [Journal]
  31. 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]
  32. Renata M. C. R. de Souza, Francisco de A. T. de Carvalho
    Clustering of interval data based on city-block distances. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2004, v:25, n:3, pp:353-365 [Journal]
  33. Francisco de A. T. de Carvalho
    Fuzzy c-means clustering methods for symbolic interval data. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2007, v:28, n:4, pp:423-437 [Journal]
  34. Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Daniel F. Pizzato
    A Partitioning Method for Mixed Feature-Type Symbolic Data Using a Squared Euclidean Distance. [Citation Graph (0, 0)][DBLP]
    KI, 2006, pp:260-273 [Conf]

  35. Vers la simulation et la détection des changements des données évolutives d'usage du Web. [Citation Graph (, )][DBLP]


  36. Application of a Hybrid Classifier to the Recognition of Petrochemical Odors. [Citation Graph (, )][DBLP]


  37. A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances. [Citation Graph (, )][DBLP]


  38. A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances. [Citation Graph (, )][DBLP]


  39. A Weighted Partitioning Dynamic Clustering Algorithm for Quantitative Feature Data Based on Adaptive Euclidean Distances. [Citation Graph (, )][DBLP]


  40. Neural Networks and Exponential Smoothing Models for Symbolic Interval Time Series Processing - Applications in Stock Market. [Citation Graph (, )][DBLP]


  41. An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data. [Citation Graph (, )][DBLP]


  42. Inequality Constraints in Regression Models to Symbolic Interval Variables. [Citation Graph (, )][DBLP]


  43. Clustering of symbolic interval data based on a single adaptive L1 distance. [Citation Graph (, )][DBLP]


  44. Hybrid model with dynamic architecture for forecasting time series. [Citation Graph (, )][DBLP]


  45. Evolving both size and accuracy of RBF networks using Memetic Algorithm. [Citation Graph (, )][DBLP]


  46. An evolutionary approach for the clustering data problem. [Citation Graph (, )][DBLP]


  47. Clustering of symbolic data through a dissimilarity volume based measure. [Citation Graph (, )][DBLP]


  48. Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data. [Citation Graph (, )][DBLP]


  49. A dynamical clustering method for symbolic interval data based on a single adaptive Euclidean distance. [Citation Graph (, )][DBLP]


  50. Symbolic interval time series forecasting using a hybrid model. [Citation Graph (, )][DBLP]


  51. Fuzzy clustering algorithms for symbolic interval data based on adaptive and non-adaptive Euclidean distances. [Citation Graph (, )][DBLP]


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


  53. Linear Regression Methods to Predict Interval-Valued Data. [Citation Graph (, )][DBLP]


  54. Constrained linear regression models for interval-valued data with dependence. [Citation Graph (, )][DBLP]


  55. Clustering symbolic interval data based on a single adaptive hausdorff distance. [Citation Graph (, )][DBLP]


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