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Note: Links lead to the DBLP on the Web. Saso Dzeroski 70 Ljupco Todorovski , Saso Dzeroski: Combining Classifiers with Meta Decision Trees. Machine Learning 50 (3): 223-249 (2003) 69 Saso Dzeroski: Relational Reinforcement Learning for Agents in Worlds with Objects. Adaptive Agents and Multi-Agents Systems 2002 : 306-322 68 Ljupco Todorovski , Hendrik Blockeel , Saso Dzeroski: Ranking with Predictive Clustering Trees. ECML 2002 : 444-455 67 Bernard Zenko , Saso Dzeroski: Stacking with an Extended Set of Meta-level Attributes and MLR. ECML 2002 : 493-504 66 Kurt Driessens , Saso Dzeroski: Integrating Experimentation and Guidance in Relational Reinforcement Learning. ICML 2002 : 115-122 65 Saso Dzeroski, Bernard Zenko : Is Combining Classifiers Better than Selecting the Best One. ICML 2002 : 123-130 64 Pat Langley , Javier Sánchez , Ljupco Todorovski , Saso Dzeroski: Inducing Process Models from Continuous Data. ICML 2002 : 347-354 63 Saso Dzeroski: Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. ILP 2002 : 346-349 62 Saso Dzeroski, Bernard Zenko : Stacking with Multi-response Model Trees. Multiple Classifier Systems 2002 : 201-211 61 Saso Dzeroski, Luc De Raedt : Multi-Relational Data Mining: a Workshop Report. SIGKDD Explorations 4 (2): 122-124 (2002) 60 Ljupco Todorovski , Saso Dzeroski: Theory Revision in Equation Discovery. Discovery Science 2001 : 389-400 59 Saso Dzeroski, Pat Langley : Computational Discovery of Communicable Knowledge: Symposium Report. Discovery Science 2001 : 45-49 58 Ljupco Todorovski , Saso Dzeroski: Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. ECML 2001 : 478-490 57 Bernard Zenko , Ljupco Todorovski , Saso Dzeroski: A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods. ICDM 2001 : 669-670 56 Joaquim Comas , Saso Dzeroski, Karina Gibert , Ignasi R.-Roda , Miquel Sànchez-Marrè : Knowledge discovery by means of inductive methods in wastewater treatment plannt data. AI Communications 14 (1): 45-62 (2001) 55 Saso Dzeroski, Luc De Raedt , Kurt Driessens : Relational Reinforcement Learning. Machine Learning 43 (1/2): 7-52 (2001) 54 Peter A. Flach , Saso Dzeroski: Editorial: Inductive Logic Programming is Coming of Age. Machine Learning 44 (3): 207-209 (2001) 53 James Cussens , Saso Dzeroski: Learning Language in Logic. Springer 2000 52 Ljupco Todorovski , Saso Dzeroski, Ashwin Srinivasan , Jonathan Whiteley , David Gavaghan : Discovering the Structure of Partial Differential Equations from Example Behaviour. ICML 2000 : 991-998 51 Dimitar Hristovski , Saso Dzeroski, Borut Peterlin , Anamarija Rozic-Hristovski : Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases. PKDD 2000 : 446-451 50 Ljupco Todorovski , Saso Dzeroski: Combining Multiple Models with Meta Decision Trees. PKDD 2000 : 54-64 49 Dragan Gamberger , Nada Lavrac , Saso Dzeroski: Noise Detection and Elimination in data Proprocessing: Experiments in Medical Domains. Applied Artificial Intelligence 14 (2): 205-223 (2000) 48 Saso Dzeroski, Damjan Demsar , Jasna Grbovic : Predicting Chemical Parameters of River Water Quality from Bioindicator Data. Applied Intelligence 13 (1): 7-17 (2000) 47 Ivan Bratko , Saso Dzeroski: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999. Morgan Kaufmann 1999 46 Saso Dzeroski, Peter A. Flach : Inductive Logic Programming, 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings. Springer 1999 45 James Cussens , Saso Dzeroski, Tomaz Erjavec : Morphosyntactic Tagging of Slovene Using Progol. ILP 1999 : 68-79 44 Saso Dzeroski, Hendrik Blockeel , Boris Kompare , Stefan Kramer , Bernhard Pfahringer , Wim Van Laer : Experiments in Predicting Biodegradability. ILP 1999 : 80-91 43 Saso Dzeroski, James Cussens , Suresh Manandhar : An Introduction to Inductive Logic Programming and Learning Language in Logic. Learning Language in Logic 1999 : 3-35 42 Saso Dzeroski, Tomaz Erjavec : Learning to Lemmatise Slovene Words. Learning Language in Logic 1999 : 69-88 41 Hendrik Blockeel , Saso Dzeroski, Jasna Grbovic : Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE. PKDD 1999 : 32-40 40 Ljupco Todorovski , Saso Dzeroski: Experiments in Meta-level Learning with ILP. PKDD 1999 : 98-106 39 Saso Dzeroski, Nada Lavrac : Editorial. Data Mining and Knowledge Discovery 3 (1): 5-6 (1999) 38 Nada Lavrac , Saso Dzeroski, Masayuki Numao : Inductive Logic Programming for Relational Knowledge Discovery. New Generation Computing 17 (1): 3-23 (1999) 37 Saso Dzeroski, Nico Jacobs , Martín Molina , Carlos Moure : ILP Experiments in Detecting Traffic Problems. ECML 1998 : 61-66 36 Saso Dzeroski, Luc De Raedt , Hendrik Blockeel : Relational Reinforcement Learning. ICML 1998 : 136-143 35 Saso Dzeroski, Luc De Raedt , Hendrik Blockeel : Relational Reinforcement Learning. ILP 1998 : 11-22 34 Suresh Manandhar , Saso Dzeroski, Tomaz Erjavec : Learning Multilingual Morphology with CLOG. ILP 1998 : 135-144 33 Saso Dzeroski, Nico Jacobs , Martín Molina , Carlos Moure , Stephen Muggleton , Wim Van Laer : Detecting Traffic Problems with ILP. ILP 1998 : 281-290 32 Blaz Zupan , Saso Dzeroski: Acquiring background knowledge for machine learning using function decomposition: a case study in rheumatology. Artificial Intelligence in Medicine 14 (1-2): 101-117 (1998) 31 Nada Lavrac , Saso Dzeroski: Inductive Logic Programming, 7th International Workshop, ILP-97, Prague, Czech Republic, September 17-20, 1997, Proceedings. Springer 1997 30 Blaz Zupan , Saso Dzeroski: Acquiring and Validating Background Knowledge for Machine Learning Using Function Decomposition. AIME 1997 : 86-97 29 Saso Dzeroski, Giorgos Potamias , Vassilis Moustakis , Giorgos Charissis : Automated Revision of Expert Rules for Treating Acute Abdominal Pain in Children. AIME 1997 : 98-109 28 Ljupco Todorovski , Saso Dzeroski: Declarative Bias in Equation Discovery. ICML 1997 : 376-384 27 Yannis Dimopoulos , Saso Dzeroski, Antonis C. Kakas : Integrating Explanatory and Descriptive Learning in ILP. IJCAI (2) 1997 : 900-907 26 Saso Dzeroski, Tomaz Erjavec : Induction of Slovene Nominal Paradigms. ILP 1997 : 141-148 25 Wim Van Laer , Luc De Raedt , Saso Dzeroski: On Multi-class Problems and Discretization in Inductive Logic Programming. ISMIS 1997 : 277-286 24 Dragan Gamberger , Nada Lavrac , Saso Dzeroski: Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. ALT 1996 : 199-212 23 Saso Dzeroski: Inductive Logic Programming and Knowledge Discovery in Databases. Advances in Knowledge Discovery and Data Mining 1996 : 117-152 22 Saso Dzeroski, Steffen Schulze-Kremer , Karsten R. Heidtke , Karsten Siems , Dietrich Wettschereck : Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra. Inductive Logic Programming Workshop 1996 : 41-54 21 Nada Lavrac , Irene Weber , Darko Zupanic , Dimitar Kazakov , Olga Stepánková , Saso Dzeroski: ILPNET Repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. AI Communications 9 (4): 157-206 (1996) 20 Nada Lavrac , Saso Dzeroski: A Reply to Pazzani's Book Review of ``Inductive Logic Programming: Techniques and Applications''. Machine Learning 23 (1): 109-111 (1996) 19 Saso Dzeroski, Ljupco Todorovski , Tanja Urbancic : Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract). ECML 1995 : 283-286 18 Saso Dzeroski: Knowledge Discovery in a Water Quality Database. KDD 1995 : 81-86 17 Saso Dzeroski: Learning First-order Clausal Theories in the Presence of Noise. SCAI 1995 : 51-60 16 Saso Dzeroski, Ljupco Todorovski : Discovering Dynamics: From Inductive Logic Programming to Machine Discovery. JIIS 4 (1): 89-108 (1995) 15 Ivan Bratko , Saso Dzeroski: Engineering Applications of ILP. New Generation Computing 13 (3&4): 313-333 (1995) 14 Saso Dzeroski, Igor Petrovski : Discovering Dynamics with Genetic Programming. ECML 1994 : 347-350 13 Luc De Raedt , Saso Dzeroski: First-Order jk-Clausal Theories are PAC-Learnable. Artificial Intelligence 70 (1-2): 375-392 (1994) 12 Jörg-Uwe Kietz , Saso Dzeroski: Inductive Logic Programming and Learnability. SIGART Bulletin 5 (1): 22-32 (1994) 11 Saso Dzeroski, Stephen Muggleton , Stuart J. Russell : Learnability of Constrained Logic Programs. ECML 1993 : 342-347 10 Saso Dzeroski, Ljupco Todorovski : Discovering Dynamics. ICML 1993 : 97-103 9 Luc De Raedt , Nada Lavrac , Saso Dzeroski: Multiple Predicate Learning. IJCAI 1993 : 1037-1043 8 Saso Dzeroski: Handling Imperfetc Data in Inductive Logic Programming. SCAI 1993 : 111-125 7 Nada Lavrac , Saso Dzeroski, Vladimir Pirnat , Viljem Krizman : The utility of background knowledge in learning medical diagnostic rules. Applied Artificial Intelligence 7 (3): 273-293 (1993) 6 Saso Dzeroski, Nada Lavrac : Inductive Learning in Deductive Databases. TKDE 5 (6): 939-949 (1993) 5 Nada Lavrac , Saso Dzeroski: Background Knowledge and Declarative Bias in Inductive Concept Learning. AII 1992 : 51-71 4 Saso Dzeroski, Stephen Muggleton , Stuart J. Russell : PAC-Learnability of Determinate Logic Programs. COLT 1992 : 128-135 3 Nada Lavrac , Saso Dzeroski, Marko Grobelnik : Learning Nonrecursive Definitions of Relations with LINUS. EWSL 1991 : 265-281 2 Saso Dzeroski, Nada Lavrac : Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. ML 1991 : 399-402 1 Nada Lavrac , Saso Dzeroski, Vladimir Pirnat , Viljem Krizman : Learning Rules for Early Diagnosis of Rheumatic Diseases. SCAI 1991 : 138-149  DiSC'03 © 2003 Association for Computing Machinery |
