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Bernhard Pfahringer: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Stefan Kramer, Bernhard Pfahringer
    Efficient Search for Strong Partial Determinations. [Citation Graph (1, 0)][DBLP]
    KDD, 1996, pp:371-374 [Conf]
  2. Bernhard Pfahringer, Stefan Kramer
    Compression-Based Evaluation of Partial Determinations. [Citation Graph (1, 0)][DBLP]
    KDD, 1995, pp:234-239 [Conf]
  3. Ashraf M. Kibriya, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes
    Multinomial Naive Bayes for Text Categorization Revisited. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:488-499 [Conf]
  4. Mi Li, Geoffrey Holmes, Bernhard Pfahringer
    Clustering Large Datasets Using Cobweb and K-Means in Tandem. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:368-379 [Conf]
  5. Bernhard Pfahringer, Geoffrey Holmes, Gabi Schmidberger
    Wrapping Boosters against Noise. [Citation Graph (0, 0)][DBLP]
    Australian Joint Conference on Artificial Intelligence, 2001, pp:402-413 [Conf]
  6. Peter Reutemann, Bernhard Pfahringer, Eibe Frank
    A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2004, pp:1017-1023 [Conf]
  7. Bernhard Pfahringer
    The Logical Way to Build a DL-based KR System. [Citation Graph (0, 0)][DBLP]
    Description Logics, 1992, pp:76-77 [Conf]
  8. Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer
    Learning to Use Operational Advice. [Citation Graph (0, 0)][DBLP]
    ECAI, 2000, pp:291-295 [Conf]
  9. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall
    Multiclass Alternating Decision Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2002, pp:161-172 [Conf]
  10. Bernhard Pfahringer
    Controlling Constructive Induction in CIPF: An MDL Approach. [Citation Graph (0, 0)][DBLP]
    ECML, 1994, pp:242-256 [Conf]
  11. Bernhard Pfahringer
    A New MDL Measure for Robust Rule Induction (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    ECML, 1995, pp:331-334 [Conf]
  12. Bernhard Pfahringer
    Compression-Based Pruning of Decision Lists. [Citation Graph (0, 0)][DBLP]
    ECML, 1997, pp:199-212 [Conf]
  13. Nils Weidmann, Eibe Frank, Bernhard Pfahringer
    A Two-Level Learning Method for Generalized Multi-instance Problems. [Citation Graph (0, 0)][DBLP]
    ECML, 2003, pp:468-479 [Conf]
  14. Bernhard Pfahringer, Hilan Bensusan, Christophe G. Giraud-Carrier
    Meta-Learning by Landmarking Various Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:743-750 [Conf]
  15. Bernhard Pfahringer
    Compression-Based Discretization of Continuous Attributes. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:456-463 [Conf]
  16. Stefan Kramer, Bernhard Pfahringer, Christopher Helma
    Stochastic Propositionalization of Non-determinate Background Knowledge. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:80-94 [Conf]
  17. Saso Dzeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer
    Experiments in Predicting Biodegradability. [Citation Graph (0, 0)][DBLP]
    ILP, 1999, pp:80-91 [Conf]
  18. Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve
    Prediction of Ordinal Classes Using Regression Trees. [Citation Graph (0, 0)][DBLP]
    ISMIS, 2000, pp:426-434 [Conf]
  19. Stefan Kramer, Bernhard Pfahringer, Christoph Helma
    Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:223-226 [Conf]
  20. Bernhard Pfahringer
    Robust Constructive Induction. [Citation Graph (0, 0)][DBLP]
    KI, 1994, pp:118-129 [Conf]
  21. Roger Clayton, John G. Cleary, Bernhard Pfahringer, Mark Utting
    Tabling Structures for Bottom-Up Logic Programming. [Citation Graph (0, 0)][DBLP]
    LOPSTR, 2002, pp:50-51 [Conf]
  22. Ernst Buchberger, Elizabeth Garner, Wolfgang Heinz, Johannes Matiasek, Bernhard Pfahringer
    VIE-DU: Dialogue by Unification. [Citation Graph (0, 0)][DBLP]
    ÖGAI, 1991, pp:42-51 [Conf]
  23. Bernhard Pfahringer
    Extending Explanation-Based Generalization. [Citation Graph (0, 0)][DBLP]
    ÖGAI, 1989, pp:149-153 [Conf]
  24. Bernhard Pfahringer, Christian Holzbaur
    VIE-KET: Frames + Prolog. [Citation Graph (0, 0)][DBLP]
    ÖGAI, 1985, pp:132-139 [Conf]
  25. Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi
    Using Weighted Nearest Neighbor to Benefit from Unlabeled Data. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2006, pp:60-69 [Conf]
  26. Eibe Frank, Bernhard Pfahringer
    Improving on Bagging with Input Smearing. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2006, pp:97-106 [Conf]
  27. Bernhard Pfahringer, Geoffrey Holmes, Richard Kirkby
    Optimizing the Induction of Alternating Decision Trees. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2001, pp:477-487 [Conf]
  28. Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer
    Stress-Testing Hoeffding Trees. [Citation Graph (0, 0)][DBLP]
    PKDD, 2005, pp:495-502 [Conf]
  29. Maximilien Sauban, Bernhard Pfahringer
    Text Categorisation Using Document Profiling. [Citation Graph (0, 0)][DBLP]
    PKDD, 2003, pp:411-422 [Conf]
  30. Eibe Frank, Mark Hall, Bernhard Pfahringer
    Locally Weighted Naive Bayes. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:249-256 [Conf]
  31. Hendrik Blockeel, Saso Dzeroski, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer
    Experiments In Predicting Biodegradability. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 2004, v:18, n:2, pp:157-181 [Journal]
  32. Johannes Fürnkranz, Bernhard Pfahringer
    Guest Editorial: First-Order Knowledge Discovery in Databases. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1998, v:12, n:5, pp:345-361 [Journal]
  33. Bernhard Pfahringer, M. Hoberstorfer, Robert Trappl
    A decision support system for village health workers in developing countries. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1988, v:2, n:1, pp:47-63 [Journal]
  34. Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, Michael de Groeve
    Prediction of Ordinal Classes Using Regression Trees. [Citation Graph (0, 0)][DBLP]
    Fundam. Inform., 2001, v:47, n:1-2, pp:1-13 [Journal]
  35. Bernhard Pfahringer
    Winning the KDD99 Classification Cup: Bagged Boosting. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2000, v:1, n:2, pp:65-66 [Journal]
  36. Bernhard Pfahringer
    The Weka solution to the 2004 KDD Cup. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:2, pp:117-119 [Journal]
  37. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby
    Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams. [Citation Graph (0, 0)][DBLP]
    CITA, 2005, pp:130-36 [Conf]
  38. Bernhard Pfahringer, Claire Leschi, Peter Reutemann
    Scaling Up Semi-supervised Learning: An Efficient and Effective LLGC Variant. [Citation Graph (0, 0)][DBLP]
    PAKDD, 2007, pp:236-247 [Conf]

  39. New Options for Hoeffding Trees. [Citation Graph (, )][DBLP]


  40. Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis. [Citation Graph (, )][DBLP]


  41. The Positive Effects of Negative Information: Extending One-Class Classification Models in Binary Proteomic Sequence Classification. [Citation Graph (, )][DBLP]


  42. Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search. [Citation Graph (, )][DBLP]


  43. Multi-label Classification Using Ensembles of Pruned Sets. [Citation Graph (, )][DBLP]


  44. Relational Random Forests Based on Random Relational Rules. [Citation Graph (, )][DBLP]


  45. Clustering Relational Data Based on Randomized Propositionalization. [Citation Graph (, )][DBLP]


  46. Organizing the World's Machine Learning Information. [Citation Graph (, )][DBLP]


  47. New ensemble methods for evolving data streams. [Citation Graph (, )][DBLP]


  48. Handling Numeric Attributes in Hoeffding Trees. [Citation Graph (, )][DBLP]


  49. Exploiting Propositionalization Based on Random Relational Rules for Semi-supervised Learning. [Citation Graph (, )][DBLP]


  50. Fast Perceptron Decision Tree Learning from Evolving Data Streams. [Citation Graph (, )][DBLP]


  51. Classifier Chains for Multi-label Classification. [Citation Graph (, )][DBLP]


  52. Leveraging Bagging for Evolving Data Streams. [Citation Graph (, )][DBLP]


  53. Learning from the Past with Experiment Databases. [Citation Graph (, )][DBLP]


  54. Improving Adaptive Bagging Methods for Evolving Data Streams. [Citation Graph (, )][DBLP]


  55. Searching for Patterns in Political Event Sequences: Experiments with the Keds Database. [Citation Graph (, )][DBLP]


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