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Note: Links lead to the DBLP on the Web. Saso Dzeroski Kurt Driessens , Saso Dzeroski: Combining Model-Based and Instance-Based Learning for First Order Regression. BNAIC 2005 : 341-342 Jan Struyf , Saso Dzeroski, Hendrik Blockeel , Amanda Clare : Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. EPIA 2005 : 272-283 Kurt Driessens , Saso Dzeroski: Combining model-based and instance-based learning for first order regression. ICML 2005 : 193-200 Jan Struyf , Saso Dzeroski: Constraint Based Induction of Multi-objective Regression Trees. KDID 2005 : 222-233 Bernard Zenko , Saso Dzeroski, Jan Struyf : Learning Predictive Clustering Rules. KDID 2005 : 234-250 Saso Dzeroski: Relational Data Mining. The Data Mining and Knowledge Discovery Handbook 2005 : 869-898 Hendrik Blockeel , Saso Dzeroski: Multi-Relational Data Mining 2005: workshop report. SIGKDD Explorations 7 (2): 126-128 (2005) Saso Dzeroski, Ljupco Todorovski , Peter Ljubic : Inductive Queries on Polynomial Equations. Constraint-Based Mining and Inductive Databases 2004 : 127-154 Saso Dzeroski, Ljupco Todorovski , Peter Ljubic : Inductive Databases of Polynomial Equations. DaWaK 2004 : 159-168 Ljupco Todorovski , Peter Ljubic , Saso Dzeroski: Inducing Polynomial Equations for Regression. ECML 2004 : 441-452 Celine Vens , Anneleen Van Assche , Hendrik Blockeel , Saso Dzeroski: First Order Random Forests with Complex Aggregates. ILP 2004 : 323-340 Nada Lavrac , Filip Zelezný , Saso Dzeroski: Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. Local Pattern Detection 2004 : 71-88 Tomaz Erjavec , Saso Dzeroski: Machine Learning of Morphosyntactic Structure: Lemmatizing Unknown Slovene Words. Applied Artificial Intelligence 18 (1): 17-41 (2004) Hendrik Blockeel , Saso Dzeroski, Boris Kompare , Stefan Kramer , Bernhard Pfahringer , Wim Van Laer : Experiments In Predicting Biodegradability. Applied Artificial Intelligence 18 (2): 157-181 (2004) Saso Dzeroski, Bernard Zenko : Is Combining Classifiers with Stacking Better than Selecting the Best One? Machine Learning 54 (3): 255-273 (2004) Kurt Driessens , Saso Dzeroski: Integrating Guidance into Relational Reinforcement Learning. Machine Learning 57 (3): 271-304 (2004) Saso Dzeroski, Hendrik Blockeel : Multi-relational data mining 2004: workshop report. SIGKDD Explorations 6 (2): 140-141 (2004) Saso Dzeroski, Bernard Zenko , Marko Debeljak : A report on the fourth international workshop on environmental applications of machine learning (EAML 2004). SIGKDD Explorations 6 (2): 155-156 (2004) Jean-François Boulicaut , Saso Dzeroski: Proceedings of the Second International Workshop on Inductive Databases, 22 September, Cavtat-Dubrovnik, Croatia Rudjer Boskovic Institute, Zagreb, Croatia 2003 Saso Dzeroski, Ljupco Todorovski , Peter Ljubic : Using Constraints in Discovering Dynamics. Discovery Science 2003 : 297-305 Saso Dzeroski, Ljupco Todorovski , Boris Zmazek , Janja Vaupotic , Ivan Kobal : Modelling Soil Radon Concentration for Earthquake Prediction. Discovery Science 2003 : 87-99 Ljupco Todorovski , Saso Dzeroski: Using Domain Specific Knowledge for Automated Modeling. IDA 2003 : 48-59 Saso Dzeroski, Ljupco Todorovski , Peter Ljubic : Inductive Databases of Polynomial Equations. KDID 2003 : 28-43 Ljupco Todorovski , Saso Dzeroski: Combining Classifiers with Meta Decision Trees. Machine Learning 50 (3): 223-249 (2003) Saso Dzeroski: Multi-relational data mining: an introduction. SIGKDD Explorations 5 (1): 1-16 (2003) Saso Dzeroski, Luc De Raedt : Multi-relational data mining: the current frontiers. SIGKDD Explorations 5 (1): 100-101 (2003) Saso Dzeroski, Luc De Raedt , Stefan Wrobel : Multirelational data mining 2003: workshop report. SIGKDD Explorations 5 (2): 200-202 (2003) Saso Dzeroski: Relational Reinforcement Learning for Agents in Worlds with Objects. Adaptive Agents and Multi-Agents Systems 2002 : 306-322 Ljupco Todorovski , Hendrik Blockeel , Saso Dzeroski: Ranking with Predictive Clustering Trees. ECML 2002 : 444-455 Bernard Zenko , Saso Dzeroski: Stacking with an Extended Set of Meta-level Attributes and MLR. ECML 2002 : 493-504 Kurt Driessens , Saso Dzeroski: Integrating Experimentation and Guidance in Relational Reinforcement Learning. ICML 2002 : 115-122 Saso Dzeroski, Bernard Zenko : Is Combining Classifiers Better than Selecting the Best One. ICML 2002 : 123-130 Pat Langley , Javier Nicolás Sánchez , Ljupco Todorovski , Saso Dzeroski: Inducing Process Models from Continuous Data. ICML 2002 : 347-354 Saso Dzeroski: Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. ILP 2002 : 346-349 Saso Dzeroski, Bernard Zenko : Stacking with Multi-response Model Trees. Multiple Classifier Systems 2002 : 201-211 Saso Dzeroski, Luc De Raedt : Multi-Relational Data Mining: a Workshop Report. SIGKDD Explorations 4 (2): 122-124 (2002) Ljupco Todorovski , Saso Dzeroski: Theory Revision in Equation Discovery. Discovery Science 2001 : 389-400 Saso Dzeroski, Pat Langley : Computational Discovery of Communicable Knowledge: Symposium Report. Discovery Science 2001 : 45-49 Ljupco Todorovski , Saso Dzeroski: Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. ECML 2001 : 478-490 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 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 Commun. 14 (1): 45-62 (2001) Saso Dzeroski, Luc De Raedt , Kurt Driessens : Relational Reinforcement Learning. Machine Learning 43 (1/2): 7-52 (2001) Peter A. Flach , Saso Dzeroski: Editorial: Inductive Logic Programming is Coming of Age. Machine Learning 44 (3): 207-209 (2001) James Cussens , Saso Dzeroski: Learning Language in Logic Springer 2000 Ljupco Todorovski , Saso Dzeroski, Ashwin Srinivasan , Jonathan Whiteley , David Gavaghan : Discovering the Structure of Partial Differential Equations from Example Behaviour. ICML 2000 : 991-998 Dimitar Hristovski , Saso Dzeroski, Borut Peterlin , Anamarija Rozic-Hristovski : Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases. PKDD 2000 : 446-451 Ljupco Todorovski , Saso Dzeroski: Combining Multiple Models with Meta Decision Trees. PKDD 2000 : 54-64 Saso Dzeroski, Damjan Demsar , Jasna Grbovic : Predicting Chemical Parameters of River Water Quality from Bioindicator Data. Appl. Intell. 13 (1): 7-17 (2000) 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) Ivan Bratko , Saso Dzeroski: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999 Morgan Kaufmann 1999 Saso Dzeroski, Peter A. Flach : Inductive Logic Programming, 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings Springer 1999 James Cussens , Saso Dzeroski, Tomaz Erjavec : Morphosyntactic Tagging of Slovene Using Progol. ILP 1999 : 68-79 Saso Dzeroski, Hendrik Blockeel , Boris Kompare , Stefan Kramer , Bernhard Pfahringer , Wim Van Laer : Experiments in Predicting Biodegradability. ILP 1999 : 80-91 Saso Dzeroski, James Cussens , Suresh Manandhar : An Introduction to Inductive Logic Programming and Learning Language in Logic. Learning Language in Logic 1999 : 3-35 Saso Dzeroski, Tomaz Erjavec : Learning to Lemmatise Slovene Words. Learning Language in Logic 1999 : 69-88 Hendrik Blockeel , Saso Dzeroski, Jasna Grbovic : Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE. PKDD 1999 : 32-40 Ljupco Todorovski , Saso Dzeroski: Experiments in Meta-level Learning with ILP. PKDD 1999 : 98-106 Saso Dzeroski, Nada Lavrac : Editorial. Data Min. Knowl. Discov. 3 (1): 5-6 (1999) Nada Lavrac , Saso Dzeroski, Masayuki Numao : Inductive Logic Programming for Relational Knowledge Discovery. New Generation Comput. 17 (1): 3-23 (1999) Saso Dzeroski, Nico Jacobs , Martín Molina , Carlos Moure : ILP Experiments in Detecting Traffic Problems. ECML 1998 : 61-66 Saso Dzeroski, Luc De Raedt , Hendrik Blockeel : Relational Reinforcement Learning. ICML 1998 : 136-143 Saso Dzeroski, Luc De Raedt , Hendrik Blockeel : Relational Reinforcement Learning. ILP 1998 : 11-22 Suresh Manandhar , Saso Dzeroski, Tomaz Erjavec : Learning Multilingual Morphology with CLOG. ILP 1998 : 135-144 Saso Dzeroski, Nico Jacobs , Martín Molina , Carlos Moure , Stephen Muggleton , Wim Van Laer : Detecting Traffic Problems with ILP. ILP 1998 : 281-290 Saso Dzeroski, Steffen Schulze-Kremer , Karsten R. Heidtke , Karsten Siems , Dietrich Wettschereck , Hendrik Blockeel : Diterpene Structure Elucidation from 13CNMR Spectra with Inductive Logic Programming. Applied Artificial Intelligence 12 (5): 363-383 (1998) 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) Nada Lavrac , Saso Dzeroski: Inductive Logic Programming, 7th International Workshop, ILP-97, Prague, Czech Republic, September 17-20, 1997, Proceedings Springer 1997 Blaz Zupan , Saso Dzeroski: Acquiring and Validating Background Knowledge for Machine Learning Using Function Decomposition. AIME 1997 : 86-97 Saso Dzeroski, George Potamias , Vassilis Moustakis , Giorgos Charissis : Automated Revision of Expert Rules for Treating Acute Abdominal Pain in Children. AIME 1997 : 98-109 Ljupco Todorovski , Saso Dzeroski: Declarative Bias in Equation Discovery. ICML 1997 : 376-384 Yannis Dimopoulos , Saso Dzeroski, Antonis C. Kakas : Integrating Explanatory and Descriptive Learning in ILP. IJCAI (2) 1997 : 900-907 Saso Dzeroski, Tomaz Erjavec : Induction of Slovene Nominal Paradigms. ILP 1997 : 141-148 Wim Van Laer , Luc De Raedt , Saso Dzeroski: On Multi-class Problems and Discretization in Inductive Logic Programming. ISMIS 1997 : 277-286 Dragan Gamberger , Nada Lavrac , Saso Dzeroski: Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. ALT 1996 : 199-212 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 Saso Dzeroski: Inductive Logic Programming and Knowledge Discovery in Databases. Advances in Knowledge Discovery and Data Mining 1996 : 117-152 Nada Lavrac , Irene Weber , Darko Zupanic , Dimitar Kazakov , Olga Stepánková , Saso Dzeroski: ILPNET Repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. AI Commun. 9 (4): 157-206 (1996) 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) Saso Dzeroski, Ljupco Todorovski , Tanja Urbancic : Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract). ECML 1995 : 283-286 Saso Dzeroski: Knowledge Discovery in a Water Quality Database. KDD 1995 : 81-86 Saso Dzeroski: Learning First-order Clausal Theories in the Presence of Noise. SCAI 1995 : 51-60 Saso Dzeroski, Ljupco Todorovski : Discovering Dynamics: From Inductive Logic Programming to Machine Discovery. J. Intell. Inf. Syst. 4 (1): 89-108 (1995) Ivan Bratko , Saso Dzeroski: Engineering Applications of ILP. New Generation Comput. 13 (3&4): 313-333 (1995) Saso Dzeroski, Igor Petrovski : Discovering Dynamics with Genetic Programming. ECML 1994 : 347-350 Luc De Raedt , Saso Dzeroski: First-Order jk-Clausal Theories are PAC-Learnable. Artif. Intell. 70 (1-2): 375-392 (1994) Jörg-Uwe Kietz , Saso Dzeroski: Inductive Logic Programming and Learnability. SIGART Bulletin 5 (1): 22-32 (1994) Saso Dzeroski, Stephen Muggleton , Stuart J. Russell : Learnability of Constrained Logic Programs. ECML 1993 : 342-347 Saso Dzeroski, Ljupco Todorovski : Discovering Dynamics. ICML 1993 : 97-103 Luc De Raedt , Nada Lavrac , Saso Dzeroski: Multiple Predicate Learning. IJCAI 1993 : 1037-1043 Saso Dzeroski: Handling Imperfetc Data in Inductive Logic Programming. SCAI 1993 : 111-125 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) Saso Dzeroski, Nada Lavrac : Inductive Learning in Deductive Databases. IEEE Trans. Knowl. Data Eng. 5 (6): 939-949 (1993) Nada Lavrac , Saso Dzeroski: Background Knowledge and Declarative Bias in Inductive Concept Learning. AII 1992 : 51-71 Saso Dzeroski, Stephen Muggleton , Stuart J. Russell : PAC-Learnability of Determinate Logic Programs. COLT 1992 : 128-135 Nada Lavrac , Saso Dzeroski, Marko Grobelnik : Learning Nonrecursive Definitions of Relations with LINUS. EWSL 1991 : 265-281 Saso Dzeroski, Nada Lavrac : Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. ML 1991 : 399-402 Nada Lavrac , Saso Dzeroski, Vladimir Pirnat , Viljem Krizman : Learning Rules for Early Diagnosis of Rheumatic Diseases. SCAI 1991 : 138-149 1 [ 87 ] 2 [ 33 ] [ 36 ] [ 37 ] [ 42 ] [ 45 ] [ 69 ] [ 81 ] [ 84 ] [ 87 ] [ 91 ] [ 96 ] 3 [ 79 ] 4 [ 15 ] [ 48 ] 5 [ 29 ] 6 [ 96 ] 7 [ 57 ] 8 [ 44 ] [ 46 ] [ 54 ] 9 [ 80 ] 10 [ 50 ] 11 [ 27 ] 12 [ 56 ] [ 67 ] [ 82 ] [ 95 ] [ 97 ] 13 [ 26 ] [ 35 ] [ 43 ] [ 46 ] [ 85 ] 14 [ 47 ] [ 55 ] 15 [ 24 ] [ 49 ] 16 [ 53 ] 17 [ 57 ] 18 [ 42 ] [ 50 ] 19 [ 3 ] 20 [ 23 ] [ 33 ] 21 [ 52 ] 22 [ 34 ] [ 38 ] 23 [ 27 ] 24 [ 21 ] 25 [ 12 ] 26 [ 77 ] 27 [ 45 ] [ 84 ] 28 [ 45 ] [ 84 ] 29 [ 1 ] [ 7 ] 30 [ 25 ] [ 34 ] [ 45 ] [ 84 ] 31 [ 60 ] [ 65 ] 32 [ 1 ] [ 2 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 9 ] [ 20 ] [ 21 ] [ 24 ] [ 31 ] [ 39 ] [ 40 ] [ 49 ] [ 86 ] 33 [ 75 ] [ 78 ] [ 88 ] [ 89 ] [ 90 ] 34 [ 35 ] [ 44 ] 35 [ 34 ] [ 38 ] 36 [ 34 ] [ 38 ] 37 [ 29 ] 38 [ 4 ] [ 11 ] [ 34 ] 39 [ 39 ] 40 [ 52 ] 41 [ 14 ] 42 [ 45 ] [ 84 ] 43 [ 1 ] [ 7 ] 44 [ 29 ] 45 [ 9 ] [ 13 ] [ 25 ] [ 36 ] [ 37 ] [ 56 ] [ 62 ] [ 71 ] [ 72 ] 46 [ 57 ] 47 [ 52 ] 48 [ 4 ] [ 11 ] 49 [ 65 ] 50 [ 57 ] 51 [ 23 ] [ 33 ] 52 [ 23 ] [ 33 ] 53 [ 53 ] 54 [ 21 ] 55 [ 93 ] [ 94 ] [ 96 ] 56 [ 10 ] [ 16 ] [ 19 ] [ 28 ] [ 41 ] [ 51 ] [ 53 ] [ 58 ] [ 59 ] [ 61 ] [ 65 ] [ 69 ] [ 74 ] [ 75 ] [ 76 ] [ 77 ] [ 78 ] [ 88 ] [ 89 ] [ 90 ] 57 [ 19 ] 58 [ 77 ] 59 [ 87 ] 60 [ 21 ] 61 [ 23 ] [ 33 ] 62 [ 53 ] 63 [ 71 ] 64 [ 86 ] 65 [ 58 ] [ 63 ] [ 66 ] [ 68 ] [ 80 ] [ 83 ] [ 93 ] 66 [ 77 ] 67 [ 30 ] [ 32 ] 68 [ 21 ]  ©2006 Association for Computing Machinery |
