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Saso Dzeroski: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Luc De Raedt, Saso Dzeroski
    First-Order jk-Clausal Theories are PAC-Learnable. [Citation Graph (4, 0)][DBLP]
    Artif. Intell., 1994, v:70, n:1-2, pp:375-392 [Journal]
  2. Saso Dzeroski, Nada Lavrac
    Inductive Learning in Deductive Databases. [Citation Graph (2, 15)][DBLP]
    IEEE Trans. Knowl. Data Eng., 1993, v:5, n:6, pp:939-949 [Journal]
  3. Nada Lavrac, Saso Dzeroski
    Background Knowledge and Declarative Bias in Inductive Concept Learning. [Citation Graph (1, 0)][DBLP]
    AII, 1992, pp:51-71 [Conf]
  4. Nada Lavrac, Saso Dzeroski, Marko Grobelnik
    Learning Nonrecursive Definitions of Relations with LINUS. [Citation Graph (1, 0)][DBLP]
    EWSL, 1991, pp:265-281 [Conf]
  5. Saso Dzeroski, Nada Lavrac
    Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. [Citation Graph (1, 0)][DBLP]
    ML, 1991, pp:399-402 [Conf]
  6. Saso Dzeroski
    Relational Reinforcement Learning for Agents in Worlds with Objects. [Citation Graph (0, 0)][DBLP]
    Adaptive Agents and Multi-Agents Systems, 2002, pp:306-322 [Conf]
  7. Saso Dzeroski, George Potamias, Vassilis Moustakis, Giorgos Charissis
    Automated Revision of Expert Rules for Treating Acute Abdominal Pain in Children. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:98-109 [Conf]
  8. Blaz Zupan, Saso Dzeroski
    Acquiring and Validating Background Knowledge for Machine Learning Using Function Decomposition. [Citation Graph (0, 0)][DBLP]
    AIME, 1997, pp:86-97 [Conf]
  9. Dragan Gamberger, Nada Lavrac, Saso Dzeroski
    Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. [Citation Graph (0, 0)][DBLP]
    ALT, 1996, pp:199-212 [Conf]
  10. Kurt Driessens, Saso Dzeroski
    Combining Model-Based and Instance-Based Learning for First Order Regression. [Citation Graph (0, 0)][DBLP]
    BNAIC, 2005, pp:341-342 [Conf]
  11. Saso Dzeroski, Ljupco Todorovski, Peter Ljubic
    Inductive Queries on Polynomial Equations. [Citation Graph (0, 0)][DBLP]
    Constraint-Based Mining and Inductive Databases, 2004, pp:127-154 [Conf]
  12. Saso Dzeroski, Stephen Muggleton, Stuart J. Russell
    PAC-Learnability of Determinate Logic Programs. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:128-135 [Conf]
  13. Nada Lavrac, Filip Zelezný, Saso Dzeroski
    Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. [Citation Graph (0, 0)][DBLP]
    Local Pattern Detection, 2004, pp:71-88 [Conf]
  14. Saso Dzeroski, Ljupco Todorovski, Peter Ljubic
    Inductive Databases of Polynomial Equations. [Citation Graph (0, 0)][DBLP]
    DaWaK, 2004, pp:159-168 [Conf]
  15. Saso Dzeroski, Pat Langley
    Computational Discovery of Communicable Knowledge: Symposium Report. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2001, pp:45-49 [Conf]
  16. Saso Dzeroski, Ljupco Todorovski, Peter Ljubic
    Using Constraints in Discovering Dynamics. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:297-305 [Conf]
  17. Saso Dzeroski, Ljupco Todorovski, Boris Zmazek, Janja Vaupotic, Ivan Kobal
    Modelling Soil Radon Concentration for Earthquake Prediction. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2003, pp:87-99 [Conf]
  18. Taneli Mielikäinen, Pance Panov, Saso Dzeroski
    Itemset Support Queries Using Frequent Itemsets and Their Condensed Representations. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2006, pp:161-172 [Conf]
  19. Ljupco Todorovski, Saso Dzeroski
    Theory Revision in Equation Discovery. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2001, pp:389-400 [Conf]
  20. Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure
    ILP Experiments in Detecting Traffic Problems. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:61-66 [Conf]
  21. Saso Dzeroski, Stephen Muggleton, Stuart J. Russell
    Learnability of Constrained Logic Programs. [Citation Graph (0, 0)][DBLP]
    ECML, 1993, pp:342-347 [Conf]
  22. Saso Dzeroski, Igor Petrovski
    Discovering Dynamics with Genetic Programming. [Citation Graph (0, 0)][DBLP]
    ECML, 1994, pp:347-350 [Conf]
  23. Saso Dzeroski, Ljupco Todorovski, Tanja Urbancic
    Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract). [Citation Graph (0, 0)][DBLP]
    ECML, 1995, pp:283-286 [Conf]
  24. Ljupco Todorovski, Hendrik Blockeel, Saso Dzeroski
    Ranking with Predictive Clustering Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2002, pp:444-455 [Conf]
  25. Ljupco Todorovski, Saso Dzeroski
    Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. [Citation Graph (0, 0)][DBLP]
    ECML, 2001, pp:478-490 [Conf]
  26. Ljupco Todorovski, Peter Ljubic, Saso Dzeroski
    Inducing Polynomial Equations for Regression. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:441-452 [Conf]
  27. Bernard Zenko, Saso Dzeroski
    Stacking with an Extended Set of Meta-level Attributes and MLR. [Citation Graph (0, 0)][DBLP]
    ECML, 2002, pp:493-504 [Conf]
  28. Jan Struyf, Saso Dzeroski, Hendrik Blockeel, Amanda Clare
    Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. [Citation Graph (0, 0)][DBLP]
    EPIA, 2005, pp:272-283 [Conf]
  29. Bernard Zenko, Ljupco Todorovski, Saso Dzeroski
    A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:669-670 [Conf]
  30. Kurt Driessens, Saso Dzeroski
    Integrating Experimentation and Guidance in Relational Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:115-122 [Conf]
  31. Kurt Driessens, Saso Dzeroski
    Combining model-based and instance-based learning for first order regression. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:193-200 [Conf]
  32. Saso Dzeroski, Luc De Raedt, Hendrik Blockeel
    Relational Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:136-143 [Conf]
  33. Saso Dzeroski, Ljupco Todorovski
    Discovering Dynamics. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:97-103 [Conf]
  34. Saso Dzeroski, Bernard Zenko
    Is Combining Classifiers Better than Selecting the Best One. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:123-130 [Conf]
  35. Pat Langley, Javier Nicolás Sánchez, Ljupco Todorovski, Saso Dzeroski
    Inducing Process Models from Continuous Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:347-354 [Conf]
  36. Ljupco Todorovski, Saso Dzeroski
    Declarative Bias in Equation Discovery. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:376-384 [Conf]
  37. Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, David Gavaghan
    Discovering the Structure of Partial Differential Equations from Example Behaviour. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:991-998 [Conf]
  38. Ljupco Todorovski, Saso Dzeroski
    Using Domain Specific Knowledge for Automated Modeling. [Citation Graph (0, 0)][DBLP]
    IDA, 2003, pp:48-59 [Conf]
  39. Yannis Dimopoulos, Saso Dzeroski, Antonis C. Kakas
    Integrating Explanatory and Descriptive Learning in ILP. [Citation Graph (0, 0)][DBLP]
    IJCAI (2), 1997, pp:900-907 [Conf]
  40. Luc De Raedt, Nada Lavrac, Saso Dzeroski
    Multiple Predicate Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1993, pp:1037-1043 [Conf]
  41. Saso Dzeroski, Tomaz Erjavec
    Induction of Slovene Nominal Paradigms. [Citation Graph (0, 0)][DBLP]
    ILP, 1997, pp:141-148 [Conf]
  42. 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]
  43. James Cussens, Saso Dzeroski, Tomaz Erjavec
    Morphosyntactic Tagging of Slovene Using Progol. [Citation Graph (0, 0)][DBLP]
    ILP, 1999, pp:68-79 [Conf]
  44. Saso Dzeroski
    Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. [Citation Graph (0, 0)][DBLP]
    ILP, 2002, pp:346-349 [Conf]
  45. Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure, Stephen Muggleton, Wim Van Laer
    Detecting Traffic Problems with ILP. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:281-290 [Conf]
  46. Saso Dzeroski, Luc De Raedt, Hendrik Blockeel
    Relational Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:11-22 [Conf]
  47. Saso Dzeroski, Steffen Schulze-Kremer, Karsten R. Heidtke, Karsten Siems, Dietrich Wettschereck
    Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra. [Citation Graph (0, 0)][DBLP]
    Inductive Logic Programming Workshop, 1996, pp:41-54 [Conf]
  48. Suresh Manandhar, Saso Dzeroski, Tomaz Erjavec
    Learning Multilingual Morphology with CLOG. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:135-144 [Conf]
  49. Celine Vens, Anneleen Van Assche, Hendrik Blockeel, Saso Dzeroski
    First Order Random Forests with Complex Aggregates. [Citation Graph (0, 0)][DBLP]
    ILP, 2004, pp:323-340 [Conf]
  50. Wim Van Laer, Luc De Raedt, Saso Dzeroski
    On Multi-class Problems and Discretization in Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    ISMIS, 1997, pp:277-286 [Conf]
  51. Saso Dzeroski
    From Inductive Logic Programming to Relational Data Mining. [Citation Graph (0, 0)][DBLP]
    JELIA, 2006, pp:1-14 [Conf]
  52. Saso Dzeroski
    Knowledge Discovery in a Water Quality Database. [Citation Graph (0, 0)][DBLP]
    KDD, 1995, pp:81-86 [Conf]
  53. Saso Dzeroski, Ljupco Todorovski, Peter Ljubic
    Inductive Databases of Polynomial Equations. [Citation Graph (0, 0)][DBLP]
    KDID, 2003, pp:28-43 [Conf]
  54. Jan Struyf, Saso Dzeroski
    Constraint Based Induction of Multi-objective Regression Trees. [Citation Graph (0, 0)][DBLP]
    KDID, 2005, pp:222-233 [Conf]
  55. Bernard Zenko, Saso Dzeroski, Jan Struyf
    Learning Predictive Clustering Rules. [Citation Graph (0, 0)][DBLP]
    KDID, 2005, pp:234-250 [Conf]
  56. Saso Dzeroski, James Cussens, Suresh Manandhar
    An Introduction to Inductive Logic Programming and Learning Language in Logic. [Citation Graph (0, 0)][DBLP]
    Learning Language in Logic, 1999, pp:3-35 [Conf]
  57. Saso Dzeroski, Tomaz Erjavec
    Learning to Lemmatise Slovene Words. [Citation Graph (0, 0)][DBLP]
    Learning Language in Logic, 1999, pp:69-88 [Conf]
  58. Saso Dzeroski, Bernard Zenko
    Stacking with Multi-response Model Trees. [Citation Graph (0, 0)][DBLP]
    Multiple Classifier Systems, 2002, pp:201-211 [Conf]
  59. Hendrik Blockeel, Saso Dzeroski, Jasna Grbovic
    Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE. [Citation Graph (0, 0)][DBLP]
    PKDD, 1999, pp:32-40 [Conf]
  60. Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saso Dzeroski, Amanda Clare
    Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics. [Citation Graph (0, 0)][DBLP]
    PKDD, 2006, pp:18-29 [Conf]
  61. Dimitar Hristovski, Saso Dzeroski, Borut Peterlin, Anamarija Rozic-Hristovski
    Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:446-451 [Conf]
  62. Ljupco Todorovski, Saso Dzeroski
    Combining Multiple Models with Meta Decision Trees. [Citation Graph (0, 0)][DBLP]
    PKDD, 2000, pp:54-64 [Conf]
  63. Ljupco Todorovski, Saso Dzeroski
    Experiments in Meta-level Learning with ILP. [Citation Graph (0, 0)][DBLP]
    PKDD, 1999, pp:98-106 [Conf]
  64. Saso Dzeroski
    Handling Imperfetc Data in Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    SCAI, 1993, pp:111-125 [Conf]
  65. Saso Dzeroski
    Learning First-order Clausal Theories in the Presence of Noise. [Citation Graph (0, 0)][DBLP]
    SCAI, 1995, pp:51-60 [Conf]
  66. Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman
    Learning Rules for Early Diagnosis of Rheumatic Diseases. [Citation Graph (0, 0)][DBLP]
    SCAI, 1991, pp:138-149 [Conf]
  67. 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]
  68. Saso Dzeroski, Steffen Schulze-Kremer, Karsten R. Heidtke, Karsten Siems, Dietrich Wettschereck, Hendrik Blockeel
    Diterpene Structure Elucidation from 13CNMR Spectra with Inductive Logic Programming. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1998, v:12, n:5, pp:363-383 [Journal]
  69. Tomaz Erjavec, Saso Dzeroski
    Machine Learning of Morphosyntactic Structure: Lemmatizing Unknown Slovene Words. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 2004, v:18, n:1, pp:17-41 [Journal]
  70. Dragan Gamberger, Nada Lavrac, Saso Dzeroski
    Noise Detection and Elimination in data Proprocessing: Experiments in Medical Domains. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 2000, v:14, n:2, pp:205-223 [Journal]
  71. Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman
    The utility of background knowledge in learning medical diagnostic rules. [Citation Graph (0, 0)][DBLP]
    Applied Artificial Intelligence, 1993, v:7, n:3, pp:273-293 [Journal]
  72. Joaquim Comas, Saso Dzeroski, Karina Gibert, Ignasi R.-Roda, Miquel Sànchez-Marrè
    Knowledge discovery by means of inductive methods in wastewater treatment plannt data. [Citation Graph (0, 0)][DBLP]
    AI Commun., 2001, v:14, n:1, pp:45-62 [Journal]
  73. Nada Lavrac, Irene Weber, Darko Zupanic, Dimitar Kazakov, Olga Stepánková, Saso Dzeroski
    ILPNET Repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. [Citation Graph (0, 0)][DBLP]
    AI Commun., 1996, v:9, n:4, pp:157-206 [Journal]
  74. Saso Dzeroski, Damjan Demsar, Jasna Grbovic
    Predicting Chemical Parameters of River Water Quality from Bioindicator Data. [Citation Graph (0, 0)][DBLP]
    Appl. Intell., 2000, v:13, n:1, pp:7-17 [Journal]
  75. Blaz Zupan, Saso Dzeroski
    Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 1998, v:14, n:1-2, pp:101-117 [Journal]
  76. Saso Dzeroski, Nada Lavrac
    Editorial. [Citation Graph (0, 0)][DBLP]
    Data Min. Knowl. Discov., 1999, v:3, n:1, pp:5-6 [Journal]
  77. Saso Dzeroski, Ljupco Todorovski
    Discovering Dynamics: From Inductive Logic Programming to Machine Discovery. [Citation Graph (0, 0)][DBLP]
    J. Intell. Inf. Syst., 1995, v:4, n:1, pp:89-108 [Journal]
  78. 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]
  79. Kurt Driessens, Saso Dzeroski
    Integrating Guidance into Relational Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:3, pp:271-304 [Journal]
  80. Saso Dzeroski, Luc De Raedt, Kurt Driessens
    Relational Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:43, n:1/2, pp:7-52 [Journal]
  81. Saso Dzeroski, Bernard Zenko
    Is Combining Classifiers with Stacking Better than Selecting the Best One? [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:54, n:3, pp:255-273 [Journal]
  82. Nada Lavrac, Saso Dzeroski
    A Reply to Pazzani's Book Review of ``Inductive Logic Programming: Techniques and Applications''. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:23, n:1, pp:109-111 [Journal]
  83. Ljupco Todorovski, Saso Dzeroski
    Combining Classifiers with Meta Decision Trees. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:50, n:3, pp:223-249 [Journal]
  84. Anneleen Van Assche, Celine Vens, Hendrik Blockeel, Saso Dzeroski
    First order random forests: Learning relational classifiers with complex aggregates. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:64, n:1-3, pp:149-182 [Journal]
  85. Ivan Bratko, Saso Dzeroski
    Engineering Applications of ILP. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 1995, v:13, n:3&4, pp:313-333 [Journal]
  86. Nada Lavrac, Saso Dzeroski, Masayuki Numao
    Inductive Logic Programming for Relational Knowledge Discovery. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 1999, v:17, n:1, pp:3-23 [Journal]
  87. Jörg-Uwe Kietz, Saso Dzeroski
    Inductive Logic Programming and Learnability. [Citation Graph (0, 0)][DBLP]
    SIGART Bulletin, 1994, v:5, n:1, pp:22-32 [Journal]
  88. Hendrik Blockeel, Saso Dzeroski
    Multi-Relational Data Mining 2005: workshop report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2005, v:7, n:2, pp:126-128 [Journal]
  89. Saso Dzeroski
    Multi-relational data mining: an introduction. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:1, pp:1-16 [Journal]
  90. Saso Dzeroski, Hendrik Blockeel
    Multi-relational data mining 2004: workshop report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:2, pp:140-141 [Journal]
  91. Saso Dzeroski, Luc De Raedt
    Multi-Relational Data Mining: a Workshop Report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2002, v:4, n:2, pp:122-124 [Journal]
  92. Saso Dzeroski, Luc De Raedt
    Multi-relational data mining: the current frontiers. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:1, pp:100-101 [Journal]
  93. Saso Dzeroski, Luc De Raedt, Stefan Wrobel
    Multirelational data mining 2003: workshop report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:200-202 [Journal]
  94. Saso Dzeroski, Bernard Zenko, Marko Debeljak
    A report on the fourth international workshop on environmental applications of machine learning (EAML 2004). [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:2, pp:155-156 [Journal]
  95. Saso Dzeroski, Pat Langley, Ljupco Todorovski
    Computational Discovery of Scientific Knowledge. [Citation Graph (0, 0)][DBLP]
    Computational Discovery of Scientific Knowledge, 2007, pp:1-14 [Conf]
  96. Dimitar Hristovski, Borut Peterlin, Saso Dzeroski, Janez Stare
    Literature Based Discovery Support System and Its Application to Disease Gene Identification. [Citation Graph (0, 0)][DBLP]
    Computational Discovery of Scientific Knowledge, 2007, pp:307-326 [Conf]
  97. Ljupco Todorovski, Saso Dzeroski
    Integrating Domain Knowledge in Equation Discovery. [Citation Graph (0, 0)][DBLP]
    Computational Discovery of Scientific Knowledge, 2007, pp:69-97 [Conf]
  98. Jan Struyf, Saso Dzeroski
    Clustering Trees with Instance Level Constraints. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:359-370 [Conf]
  99. Annalisa Appice, Saso Dzeroski
    Stepwise Induction of Multi-target Model Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:502-509 [Conf]
  100. Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski
    Ensembles of Multi-Objective Decision Trees. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:624-631 [Conf]
  101. Pance Panov, Saso Dzeroski
    Combining Bagging and Random Subspaces to Create Better Ensembles. [Citation Graph (0, 0)][DBLP]
    IDA, 2007, pp:118-129 [Conf]
  102. Saso Dzeroski
    Towards a General Framework for Data Mining. [Citation Graph (0, 0)][DBLP]
    KDID, 2006, pp:259-300 [Conf]
  103. Saso Dzeroski, Valentin Gjorgjioski, Ivica Slavkov, Jan Struyf
    Analysis of Time Series Data with Predictive Clustering Trees. [Citation Graph (0, 0)][DBLP]
    KDID, 2006, pp:63-80 [Conf]
  104. Dragi Kocev, Jan Struyf, Saso Dzeroski
    Beam Search Induction and Similarity Constraints for Predictive Clustering Trees. [Citation Graph (0, 0)][DBLP]
    KDID, 2006, pp:134-151 [Conf]
  105. Annalisa Appice, Saso Dzeroski
    Inducing Multi-Target Model Trees in a Stepwise Fashion. [Citation Graph (0, 0)][DBLP]
    SEBD, 2007, pp:16-27 [Conf]
  106. Saso Dzeroski, Jan Struyf
    5th international workshop on knowledge discovery in inductive databases (KDID'06): workshop report. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2007, v:9, n:1, pp:56-58 [Journal]

  107. Detection of Visual Concepts and Annotation of Images Using Predictive Clustering Trees. [Citation Graph (, )][DBLP]


  108. Towards an Ontology of Data Mining Investigations. [Citation Graph (, )][DBLP]


  109. OntoDM: An Ontology of Data Mining. [Citation Graph (, )][DBLP]


  110. Rule Ensembles for Multi-target Regression. [Citation Graph (, )][DBLP]


  111. Predicting chemical parameters of the water from diatom abudance in lake Prespa and its tributaries. [Citation Graph (, )][DBLP]


  112. Learning Classification Rules for Multiple Target Attributes. [Citation Graph (, )][DBLP]


  113. A Minimal Description Length Scheme for Polynomial Regression. [Citation Graph (, )][DBLP]


  114. Predicting gene function using hierarchical multi-label decision tree ensembles. [Citation Graph (, )][DBLP]


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