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João Gama: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. João Gama, Gladys Castillo
    Learning with Local Drift Detection. [Citation Graph (0, 0)][DBLP]
    ADMA, 2006, pp:42-55 [Conf]
  2. Gladys Castillo, João Gama
    Bias Management of Bayesian Network Classifiers. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2005, pp:70-83 [Conf]
  3. Milton Severo, João Gama
    Change Detection with Kalman Filter and CUSUM. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2006, pp:243-254 [Conf]
  4. Luís Torgo, João Gama
    Search-Based Class Discretization. [Citation Graph (0, 0)][DBLP]
    ECML, 1997, pp:266-273 [Conf]
  5. Gladys Castillo, João Gama, Pedro Medas
    Adaptation to Drifting Concepts. [Citation Graph (0, 0)][DBLP]
    EPIA, 2003, pp:279-293 [Conf]
  6. João Gama, Pavel Brazdil
    Characterization of Classification Algorithms. [Citation Graph (0, 0)][DBLP]
    EPIA, 1995, pp:189-200 [Conf]
  7. João Gama, João Moura Pires, Margarida Cardoso, Nuno Cavalheiro Marques, Luís Cavique
    Introduction. [Citation Graph (0, 0)][DBLP]
    EPIA, 2005, pp:287-287 [Conf]
  8. João Gama, Gladys Castillo
    Adaptive Bayes. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2002, pp:765-774 [Conf]
  9. João Gama, Luís Torgo, Carlos Soares
    Dynamic Discretization of Continuous Attributes. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 1998, pp:160-169 [Conf]
  10. João Gama, Ricardo Rocha, Pedro Medas
    Accurate decision trees for mining high-speed data streams. [Citation Graph (0, 0)][DBLP]
    KDD, 2003, pp:523-528 [Conf]
  11. Gladys Castillo, João Gama
    An Adaptive Prequential Learning Framework for Bayesian Network Classifiers. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:67-78 [Conf]
  12. João Gama, Pedro Medas
    Learning in Dynamic Environments: Decision Trees for Data Streams. [Citation Graph (0, 0)][DBLP]
    PRIS, 2004, pp:149-158 [Conf]
  13. João Gama, Pedro Medas, Ricardo Rocha
    Forest trees for on-line data. [Citation Graph (0, 0)][DBLP]
    SAC, 2004, pp:632-636 [Conf]
  14. João Gama, Pedro Medas, Pedro Pereira Rodrigues
    Learning decision trees from dynamic data streams. [Citation Graph (0, 0)][DBLP]
    SAC, 2005, pp:573-577 [Conf]
  15. João Gama, Carlos Pinto
    Discretization from data streams: applications to histograms and data mining. [Citation Graph (0, 0)][DBLP]
    SAC, 2006, pp:662-667 [Conf]
  16. Eduardo J. Spinosa, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama
    OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams. [Citation Graph (0, 0)][DBLP]
    SAC, 2007, pp:448-452 [Conf]
  17. João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues
    Learning with Drift Detection. [Citation Graph (0, 0)][DBLP]
    SBIA, 2004, pp:286-295 [Conf]
  18. Luís Torgo, João Gama
    Regression by Classification. [Citation Graph (0, 0)][DBLP]
    SBIA, 1996, pp:51-60 [Conf]
  19. Pedro Pereira Rodrigues, João Gama, João Pedro Pedroso
    ODAC: Hierarchical Clustering of Time Series Data Streams. [Citation Graph (0, 0)][DBLP]
    SDM, 2006, pp:- [Conf]
  20. Gladys Castillo, João Gama, Ana M. Breda
    Adaptive Bayes for a Student Modeling Prediction Task Based on Learning Styles. [Citation Graph (0, 0)][DBLP]
    User Modeling, 2003, pp:328-332 [Conf]
  21. Luís Torgo, João Gama
    Regression Using Classification Algorithms. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 1997, v:1, n:1-4, pp:275-292 [Journal]
  22. João Gama, Ricardo Fernandes, Ricardo Rocha
    Decision trees for mining data streams. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2006, v:10, n:1, pp:23-45 [Journal]
  23. Jesús S. Aguilar-Ruiz, João Gama
    Data Streams J.UCS Special Issue. [Citation Graph (0, 0)][DBLP]
    J. UCS, 2005, v:11, n:8, pp:1349-1352 [Journal]
  24. João Gama, Pedro Medas
    Learning Decision Trees from Dynamic Data Streams. [Citation Graph (0, 0)][DBLP]
    J. UCS, 2005, v:11, n:8, pp:1353-1366 [Journal]
  25. João Gama
    Functional Trees. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:55, n:3, pp:219-250 [Journal]
  26. Pedro Pereira Rodrigues, João Gama
    Semi-fuzzy Splitting in Online Divisive-Agglomerative Clustering. [Citation Graph (0, 0)][DBLP]
    EPIA Workshops, 2007, pp:133-144 [Conf]
  27. Raquel Sebastião, João Gama
    Change Detection in Learning Histograms from Data Streams. [Citation Graph (0, 0)][DBLP]
    EPIA Workshops, 2007, pp:112-123 [Conf]
  28. Edgar Pimenta, João Gama, André Carlos Ponce Leon Ferreira de Carvalho
    Pursuing the Best ECOC Dimension for Multiclass Problems. [Citation Graph (0, 0)][DBLP]
    FLAIRS Conference, 2007, pp:622-627 [Conf]
  29. João Gama, Pedro Pereira Rodrigues
    Stream-Based Electricity Load Forecast. [Citation Graph (0, 0)][DBLP]
    PKDD, 2007, pp:446-453 [Conf]

  30. Sequential Pattern Mining in Multi-relational Datasets. [Citation Graph (, )][DBLP]

  31. Decision Trees Using the Minimum Entropy-of-Error Principle. [Citation Graph (, )][DBLP]

  32. Regression Trees from Data Streams with Drift Detection. [Citation Graph (, )][DBLP]

  33. Robust Division in Clustering of Streaming Time Series. [Citation Graph (, )][DBLP]

  34. Drift Severity Metric. [Citation Graph (, )][DBLP]

  35. Tracking Recurring Concepts with Meta-learners. [Citation Graph (, )][DBLP]

  36. Online Reliability Estimates for Individual Predictions in Data Streams. [Citation Graph (, )][DBLP]

  37. Change Detection in Climate Data over the Iberian Peninsula. [Citation Graph (, )][DBLP]

  38. RUSE-WARMR: Rule Selection for Classifier Induction in Multi-relational Data-Sets. [Citation Graph (, )][DBLP]

  39. Bipartite Graphs for Monitoring Clusters Transitions. [Citation Graph (, )][DBLP]

  40. Issues in evaluation of stream learning algorithms. [Citation Graph (, )][DBLP]

  41. Monitoring Incremental Histogram Distribution for Change Detection in Data Streams. [Citation Graph (, )][DBLP]

  42. A Simple Dense Pixel Visualization for Mobile Sensor Data Mining. [Citation Graph (, )][DBLP]

  43. Clustering Distributed Sensor Data Streams. [Citation Graph (, )][DBLP]

  44. Special track on Data Streams: editorial message. [Citation Graph (, )][DBLP]

  45. Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks. [Citation Graph (, )][DBLP]

  46. Evaluating algorithms that learn from data streams. [Citation Graph (, )][DBLP]

  47. Learning from Data Streams: Synopsis and Change Detection. [Citation Graph (, )][DBLP]

  48. A review on the combination of binary classifiers in multiclass problems. [Citation Graph (, )][DBLP]

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