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Peter A. Flach: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Peter A. Flach
    Second-order Inductive Learning. [Citation Graph (0, 0)][DBLP]
    AII, 1989, pp:202-216 [Conf]
  2. Peter A. Flach
    An Analysis of Various Forms of "Jumping to Conclusions". [Citation Graph (0, 0)][DBLP]
    AII, 1992, pp:170-186 [Conf]
  3. Peter A. Flach, Nada Lavrac
    Learning in Clausal Logic: A Perspective on Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    Computational Logic: Logic Programming and Beyond, 2002, pp:437-471 [Conf]
  4. Thomas Gärtner, Peter A. Flach, Stefan Wrobel
    On Graph Kernels: Hardness Results and Efficient Alternatives. [Citation Graph (0, 0)][DBLP]
    COLT, 2003, pp:129-143 [Conf]
  5. Peter A. Flach
    From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases. [Citation Graph (0, 0)][DBLP]
    Transactions and Change in Logic Databases, 1998, pp:356-387 [Conf]
  6. Elias Gyftodimos, Peter A. Flach
    Combining Bayesian Networks with Higher-Order Data Representations. [Citation Graph (0, 0)][DBLP]
    Probabilistic, Logical and Relational Learning, 2005, pp:- [Conf]
  7. Yonghong Peng, Peter A. Flach, Carlos Soares, Pavel Brazdil
    Improved Dataset Characterisation for Meta-learning. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2002, pp:141-152 [Conf]
  8. Peter A. Flach
    Inductive Logic Databases: From Extensional to Intensional Knowledge. [Citation Graph (0, 0)][DBLP]
    DOOD, 1997, pp:3- [Conf]
  9. César Ferri, Peter A. Flach, José Hernández-Orallo
    Improving the AUC of Probabilistic Estimation Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2003, pp:121-132 [Conf]
  10. Peter A. Flach
    Predicate Invention in Inductive Data Engineering. [Citation Graph (0, 0)][DBLP]
    ECML, 1993, pp:83-94 [Conf]
  11. Johannes Fürnkranz, Peter A. Flach
    An Analysis of Stopping and Filtering Criteria for Rule Learning. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:123-133 [Conf]
  12. Shan-Hwei Nienhuys-Cheng, Peter A. Flach
    Consistent Term Mappings, Term Partitions and Inverse Resolution. [Citation Graph (0, 0)][DBLP]
    EWSL, 1991, pp:361-374 [Conf]
  13. Peter A. Flach
    Knowledge Representation for Inductive Learning. [Citation Graph (0, 0)][DBLP]
    ESCQARU, 1999, pp:160-167 [Conf]
  14. Peter A. Flach
    Multi-relational Data Mining: a perspective. [Citation Graph (0, 0)][DBLP]
    EPIA, 2001, pp:3-4 [Conf]
  15. Peter A. Flach
    Reinventing Machine Learning with ROC Analysis. [Citation Graph (0, 0)][DBLP]
    IBERAMIA-SBIA, 2006, pp:4-5 [Conf]
  16. Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski
    Adapting classification rule induction to subgroup discovery. [Citation Graph (0, 0)][DBLP]
    ICDM, 2002, pp:266-273 [Conf]
  17. Annalisa Appice, Michelangelo Ceci, Simon Rawles, Peter A. Flach
    Redundant feature elimination for multi-class problems. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  18. César Ferri, Peter A. Flach, José Hernández-Orallo
    Learning Decision Trees Using the Area Under the ROC Curve. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:139-146 [Conf]
  19. César Ferri, Peter A. Flach, José Hernández-Orallo
    Delegating classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  20. Peter A. Flach
    The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:194-201 [Conf]
  21. Johannes Fürnkranz, Peter A. Flach
    An Analysis of Rule Evaluation Metrics. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:202-209 [Conf]
  22. Thomas Gärtner, Peter A. Flach
    WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:154-161 [Conf]
  23. Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola
    Multi-Instance Kernels. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:179-186 [Conf]
  24. Nicolas Lachiche, Peter A. Flach
    Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:416-423 [Conf]
  25. Elias Gyftodimos, Peter A. Flach
    Combining Bayesian Networks with Higher-Order Data Representations. [Citation Graph (0, 0)][DBLP]
    IDA, 2005, pp:145-156 [Conf]
  26. Peter A. Flach, Shaomin Wu
    Repairing Concavities in ROC Curves. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:702-707 [Conf]
  27. Ronaldo C. Prati, Peter A. Flach
    ROCCER: An Algorithm for Rule Learning Based on ROC Analysis. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:823-828 [Conf]
  28. Mark-A. Krogel, Simon Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel
    Comparative Evaluation of Approaches to Propositionalization. [Citation Graph (0, 0)][DBLP]
    ILP, 2003, pp:197-214 [Conf]
  29. Nicolas Lachiche, Peter A. Flach
    1BC2: A True First-Order Bayesian Classifier. [Citation Graph (0, 0)][DBLP]
    ILP, 2002, pp:133-148 [Conf]
  30. Nada Lavrac, Peter A. Flach, Blaz Zupan
    Rule Evaluation Measures: A Unifying View. [Citation Graph (0, 0)][DBLP]
    ILP, 1999, pp:174-185 [Conf]
  31. Peter A. Flach
    Normal Forms for Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    ILP, 1997, pp:149-156 [Conf]
  32. Peter A. Flach, Christophe G. Giraud-Carrier, John W. Lloyd
    Strongly Typed Inductive Concept Learning. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:185-194 [Conf]
  33. Peter A. Flach, Nicolas Lachiche
    Decomposing Probability Distributions on Structured Individuals. [Citation Graph (0, 0)][DBLP]
    ILP Work-in-progress reports, 2000, pp:- [Conf]
  34. Peter A. Flach, Nicolas Lachiche
    IBC: A First-Order Bayesian Classifier. [Citation Graph (0, 0)][DBLP]
    ILP, 1999, pp:92-103 [Conf]
  35. Thomas Gärtner, John W. Lloyd, Peter A. Flach
    Kernels for Structured Data. [Citation Graph (0, 0)][DBLP]
    ILP, 2002, pp:66-83 [Conf]
  36. Nada Lavrac, Filip Zelezný, Peter A. Flach
    RSD: Relational Subgroup Discovery through First-Order Feature Construction. [Citation Graph (0, 0)][DBLP]
    ILP, 2002, pp:149-165 [Conf]
  37. Dimitrios Mavroeidis, Peter A. Flach
    Improved Distances for Structured Data. [Citation Graph (0, 0)][DBLP]
    ILP, 2003, pp:251-268 [Conf]
  38. Peter A. Flach
    Towards a Theory of Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1991, pp:510-519 [Conf]
  39. Peter A. Flach
    Comparing Consequence Relations. [Citation Graph (0, 0)][DBLP]
    KR, 1998, pp:180-189 [Conf]
  40. Peter A. Flach
    A Model of Inductive Reasoning. [Citation Graph (0, 0)][DBLP]
    Logic at Work, 1992, pp:41-56 [Conf]
  41. Ljupco Todorovski, Peter A. Flach, Nada Lavrac
    Predictive Performance of Weghted Relative Accuracy. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:255-264 [Conf]
  42. Elias Gyftodimos, Peter A. Flach
    Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data. [Citation Graph (0, 0)][DBLP]
    SETN, 2004, pp:291-300 [Conf]
  43. Peter A. Flach
    Rationality Postulates for Induction. [Citation Graph (0, 0)][DBLP]
    TARK, 1996, pp:267-281 [Conf]
  44. Peter A. Flach
    The Use of Functional and Logic Languages in Machine Learning. [Citation Graph (0, 0)][DBLP]
    WFLP, 2000, pp:225-237 [Conf]
  45. Peter A. Flach
    On the state of the art in machine learning: A personal review. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 2001, v:131, n:1-2, pp:199-222 [Journal]
  46. Peter A. Flach, Iztok Savnik
    Database Dependency Discovery: A Machine Learning Approach. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1999, v:12, n:3, pp:139-160 [Journal]
  47. Tanja Urbancic, Maja Skrjanc, Peter A. Flach
    Web-based analysis of data mining and decision support education. [Citation Graph (0, 0)][DBLP]
    AI Commun., 2002, v:15, n:4, pp:199-204 [Journal]
  48. Iztok Savnik, Peter A. Flach
    Discovery of multivalued dependencies from relations. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2000, v:4, n:3-4, pp:195-211 [Journal]
  49. Peter A. Flach, Antonis C. Kakas
    Abductive and Inductive Reasoning: Report of the ECAI'96 Workshop. [Citation Graph (0, 0)][DBLP]
    Logic Journal of the IGPL, 1997, v:5, n:5, pp:- [Journal]
  50. Peter A. Flach, Antonis C. Kakas
    Abduction and Induction in AI: Report of the IJCAI'97 Workshop. [Citation Graph (0, 0)][DBLP]
    Logic Journal of the IGPL, 1998, v:6, n:4, pp:651-656 [Journal]
  51. Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski
    Subgroup Discovery with CN2-SD. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:153-188 [Journal]
  52. Peter A. Flach, Saso Dzeroski
    Editorial: Inductive Logic Programming is Coming of Age. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:44, n:3, pp:207-209 [Journal]
  53. Peter A. Flach, Nicolas Lachiche
    Confirmation-Guided Discovery of First-Order Rules with Tertius. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:42, n:1/2, pp:61-95 [Journal]
  54. Peter A. Flach, Nicolas Lachiche
    Naive Bayesian Classification of Structured Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:3, pp:233-269 [Journal]
  55. Johannes Fürnkranz, Peter A. Flach
    ROC 'n' Rule Learning-Towards a Better Understanding of Covering Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:58, n:1, pp:39-77 [Journal]
  56. Thomas Gärtner, John W. Lloyd, Peter A. Flach
    Kernels and Distances for Structured Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:3, pp:205-232 [Journal]
  57. Tom Fawcett, Peter A. Flach
    A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:58, n:1, pp:33-38 [Journal]
  58. Nada Lavrac, Bojan Cestnik, Dragan Gamberger, Peter A. Flach
    Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:115-143 [Journal]
  59. José Hernández-Orallo, César Ferri, Nicolas Lachiche, Peter A. Flach
    The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004). [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:2, pp:159-161 [Journal]
  60. Nada Lavrac, Peter A. Flach
    An extended transformation approach to inductive logic programming. [Citation Graph (0, 0)][DBLP]
    ACM Trans. Comput. Log., 2001, v:2, n:4, pp:458-494 [Journal]
  61. Peter A. Flach
    Book review: Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, ISBN 3-540-42027-4. [Citation Graph (0, 0)][DBLP]
    TPLP, 2004, v:4, n:5-6, pp:753-755 [Journal]
  62. Peter A. Flach, Edson Takashi Matsubara
    A Simple Lexicographic Ranker and Probability Estimator. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:575-582 [Conf]
  63. Shaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez
    An Improved Model Selection Heuristic for AUC. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:478-489 [Conf]
  64. Kerstin Eder, Peter A. Flach, Hsiou-Wen Hsueh
    Towards Automating Simulation-Based Design Verification Using ILP. [Citation Graph (0, 0)][DBLP]
    ILP, 2006, pp:154-168 [Conf]
  65. Peter A. Flach
    Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. [Citation Graph (0, 0)][DBLP]
    ECML/PKDD, 2007, pp:2-3 [Conf]

  66. Cost-Based Sampling of Individual Instances. [Citation Graph (, )][DBLP]


  67. Unsupervised Word Decomposition with the Promodes Algorithm. [Citation Graph (, )][DBLP]


  68. Unsupervised Morpheme Discovery with Ungrade. [Citation Graph (, )][DBLP]


  69. On classification, ranking, and probability estimation. [Citation Graph (, )][DBLP]


  70. A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples. [Citation Graph (, )][DBLP]


  71. The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery. [Citation Graph (, )][DBLP]


  72. Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms. [Citation Graph (, )][DBLP]


  73. Evaluation Measures for Multi-class Subgroup Discovery. [Citation Graph (, )][DBLP]


  74. Network analysis in natural sciences and engineering. [Citation Graph (, )][DBLP]


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