| 2009 | ||
|---|---|---|
| 124 | Pance Panov, Larisa N. Soldatova, Saso Dzeroski: Towards an Ontology of Data Mining Investigations. Discovery Science 2009: 257-271 | |
| 123 | Andreja Naumoski, Dragi Kocev, Natasa Atanasova, Kosta Mitreski, Svetislav Krstic, Saso Dzeroski: Predicting chemical parameters of the water from diatom abudance in lake Prespa and its tributaries. ITEE 2009: 264-277 | |
| 122 | Saso Dzeroski, Pance Panov, Bernard Zenko: Machine Learning, Ensemble Methods in. Encyclopedia of Complexity and Systems Science 2009: 5317-5325 | |
| 2008 | ||
| 121 | Pance Panov, Saso Dzeroski, Larisa N. Soldatova: OntoDM: An Ontology of Data Mining. ICDM Workshops 2008: 752-760 | |
| 120 | Aleksandar Peckov, Saso Dzeroski, Ljupco Todorovski: A Minimal Description Length Scheme for Polynomial Regression. PAKDD 2008: 284-295 | |
| 119 | Bernard Zenko, Saso Dzeroski: Learning Classification Rules for Multiple Target Attributes. PAKDD 2008: 454-465 | |
| 118 | Will Bridewell, Pat Langley, Ljupco Todorovski, Saso Dzeroski: Inductive process modeling. Machine Learning 71(1): 1-32 (2008) | |
| 117 | Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski, Hendrik Blockeel: Decision trees for hierarchical multi-label classification. Machine Learning 73(2): 185-214 (2008) | |
| 2007 | ||
| 116 | Saso Dzeroski, Ljupco Todorovski: Computational Discovery of Scientific Knowledge, Introduction, Techniques, and Applications in Environmental and Life Sciences Springer 2007 | |
| 115 | Saso Dzeroski, Jan Struyf: Knowledge Discovery in Inductive Databases, 5th International Workshop, KDID 2006, Berlin, Germany, September 18, 2006, Revised Selected and Invited Papers Springer 2007 | |
| 114 | Saso Dzeroski, Pat Langley, Ljupco Todorovski: Computational Discovery of Scientific Knowledge. Computational Discovery of Scientific Knowledge 2007: 1-14 | |
| 113 | Dimitar Hristovski, Borut Peterlin, Saso Dzeroski, Janez Stare: Literature Based Discovery Support System and Its Application to Disease Gene Identification. Computational Discovery of Scientific Knowledge 2007: 307-326 | |
| 112 | Ljupco Todorovski, Saso Dzeroski: Integrating Domain Knowledge in Equation Discovery. Computational Discovery of Scientific Knowledge 2007: 69-97 | |
| 111 | Jan Struyf, Saso Dzeroski: Clustering Trees with Instance Level Constraints. ECML 2007: 359-370 | |
| 110 | Annalisa Appice, Saso Dzeroski: Stepwise Induction of Multi-target Model Trees. ECML 2007: 502-509 | |
| 109 | Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski: Ensembles of Multi-Objective Decision Trees. ECML 2007: 624-631 | |
| 108 | Pance Panov, Saso Dzeroski: Combining Bagging and Random Subspaces to Create Better Ensembles. IDA 2007: 118-129 | |
| 107 | Annalisa Appice, Saso Dzeroski: Inducing Multi-Target Model Trees in a Stepwise Fashion. SEBD 2007: 16-27 | |
| 106 | Katerina Taskova, Daniela Stojanova, Marko Bohanec, Saso Dzeroski: A Qualitative Decision-Support Model for Evaluating Researchers. Informatica (Slovenia) 31(4): 479-486 (2007) | |
| 105 | Saso Dzeroski, Jan Struyf: 5th international workshop on knowledge discovery in inductive databases (KDID'06): workshop report. SIGKDD Explorations 9(1): 56-58 (2007) | |
| 2006 | ||
| 104 | Taneli Mielikäinen, Pance Panov, Saso Dzeroski: Itemset Support Queries Using Frequent Itemsets and Their Condensed Representations. Discovery Science 2006: 161-172 | |
| 103 | Saso Dzeroski: From Inductive Logic Programming to Relational Data Mining. JELIA 2006: 1-14 | |
| 102 | Dragi Kocev, Jan Struyf, Saso Dzeroski: Beam Search Induction and Similarity Constraints for Predictive Clustering Trees. KDID 2006: 134-151 | |
| 101 | Saso Dzeroski: Towards a General Framework for Data Mining. KDID 2006: 259-300 | |
| 100 | Saso Dzeroski, Valentin Gjorgjioski, Ivica Slavkov, Jan Struyf: Analysis of Time Series Data with Predictive Clustering Trees. KDID 2006: 63-80 | |
| 99 | Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saso Dzeroski, Amanda Clare: Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics. PKDD 2006: 18-29 | |
| 98 | Anneleen Van Assche, Celine Vens, Hendrik Blockeel, Saso Dzeroski: First order random forests: Learning relational classifiers with complex aggregates. Machine Learning 64(1-3): 149-182 (2006) | |
| 2005 | ||
| 97 | Kurt Driessens, Saso Dzeroski: Combining Model-Based and Instance-Based Learning for First Order Regression. BNAIC 2005: 341-342 | |
| 96 | Jan Struyf, Saso Dzeroski, Hendrik Blockeel, Amanda Clare: Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. EPIA 2005: 272-283 | |
| 95 | Kurt Driessens, Saso Dzeroski: Combining model-based and instance-based learning for first order regression. ICML 2005: 193-200 | |
| 94 | Jan Struyf, Saso Dzeroski: Constraint Based Induction of Multi-objective Regression Trees. KDID 2005: 222-233 | |
| 93 | Bernard Zenko, Saso Dzeroski, Jan Struyf: Learning Predictive Clustering Rules. KDID 2005: 234-250 | |
| 92 | Saso Dzeroski: Relational Data Mining. The Data Mining and Knowledge Discovery Handbook 2005: 869-898 | |
| 91 | Hendrik Blockeel, Saso Dzeroski: Multi-Relational Data Mining 2005: workshop report. SIGKDD Explorations 7(2): 126-128 (2005) | |
| 2004 | ||
| 90 | Saso Dzeroski, Ljupco Todorovski, Peter Ljubic: Inductive Queries on Polynomial Equations. Constraint-Based Mining and Inductive Databases 2004: 127-154 | |
| 89 | Saso Dzeroski, Ljupco Todorovski, Peter Ljubic: Inductive Databases of Polynomial Equations. DaWaK 2004: 159-168 | |
| 88 | Ljupco Todorovski, Peter Ljubic, Saso Dzeroski: Inducing Polynomial Equations for Regression. ECML 2004: 441-452 | |
| 87 | Celine Vens, Anneleen Van Assche, Hendrik Blockeel, Saso Dzeroski: First Order Random Forests with Complex Aggregates. ILP 2004: 323-340 | |
| 86 | Nada Lavrac, Filip Zelezný, Saso Dzeroski: Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. Local Pattern Detection 2004: 71-88 | |
| 85 | Tomaz Erjavec, Saso Dzeroski: Machine Learning of Morphosyntactic Structure: Lemmatizing Unknown Slovene Words. Applied Artificial Intelligence 18(1): 17-41 (2004) | |
| 84 | 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) | |
| 83 | Saso Dzeroski, Bernard Zenko: Is Combining Classifiers with Stacking Better than Selecting the Best One? Machine Learning 54(3): 255-273 (2004) | |
| 82 | Kurt Driessens, Saso Dzeroski: Integrating Guidance into Relational Reinforcement Learning. Machine Learning 57(3): 271-304 (2004) | |
| 81 | Saso Dzeroski, Hendrik Blockeel: Multi-relational data mining 2004: workshop report. SIGKDD Explorations 6(2): 140-141 (2004) | |
| 80 | 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) | |
| 2003 | ||
| 79 | 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 | |
| 78 | Saso Dzeroski, Ljupco Todorovski, Peter Ljubic: Using Constraints in Discovering Dynamics. Discovery Science 2003: 297-305 | |
| 77 | Saso Dzeroski, Ljupco Todorovski, Boris Zmazek, Janja Vaupotic, Ivan Kobal: Modelling Soil Radon Concentration for Earthquake Prediction. Discovery Science 2003: 87-99 | |
| 76 | Ljupco Todorovski, Saso Dzeroski: Using Domain Specific Knowledge for Automated Modeling. IDA 2003: 48-59 | |
| 75 | Saso Dzeroski, Ljupco Todorovski, Peter Ljubic: Inductive Databases of Polynomial Equations. KDID 2003: 28-43 | |
| 74 | Ljupco Todorovski, Saso Dzeroski: Combining Classifiers with Meta Decision Trees. Machine Learning 50(3): 223-249 (2003) | |
| 73 | Saso Dzeroski: Multi-relational data mining: an introduction. SIGKDD Explorations 5(1): 1-16 (2003) | |
| 72 | Saso Dzeroski, Luc De Raedt: Multi-relational data mining: the current frontiers. SIGKDD Explorations 5(1): 100-101 (2003) | |
| 71 | Saso Dzeroski, Luc De Raedt, Stefan Wrobel: Multirelational data mining 2003: workshop report. SIGKDD Explorations 5(2): 200-202 (2003) | |
| 2002 | ||
| 70 | Saso Dzeroski: Relational Reinforcement Learning for Agents in Worlds with Objects. Adaptive Agents and Multi-Agents Systems 2002: 306-322 | |
| 69 | Ljupco Todorovski, Hendrik Blockeel, Saso Dzeroski: Ranking with Predictive Clustering Trees. ECML 2002: 444-455 | |
| 68 | Bernard Zenko, Saso Dzeroski: Stacking with an Extended Set of Meta-level Attributes and MLR. ECML 2002: 493-504 | |
| 67 | Kurt Driessens, Saso Dzeroski: Integrating Experimentation and Guidance in Relational Reinforcement Learning. ICML 2002: 115-122 | |
| 66 | Saso Dzeroski, Bernard Zenko: Is Combining Classifiers Better than Selecting the Best One. ICML 2002: 123-130 | |
| 65 | Pat Langley, Javier Nicolás Sánchez, Ljupco Todorovski, Saso Dzeroski: Inducing Process Models from Continuous Data. ICML 2002: 347-354 | |
| 64 | Saso Dzeroski: Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. ILP 2002: 346-349 | |
| 63 | Saso Dzeroski, Bernard Zenko: Stacking with Multi-response Model Trees. Multiple Classifier Systems 2002: 201-211 | |
| 62 | Saso Dzeroski, Luc De Raedt: Multi-Relational Data Mining: a Workshop Report. SIGKDD Explorations 4(2): 122-124 (2002) | |
| 2001 | ||
| 61 | Ljupco Todorovski, Saso Dzeroski: Theory Revision in Equation Discovery. Discovery Science 2001: 389-400 | |
| 60 | Saso Dzeroski, Pat Langley: Computational Discovery of Communicable Knowledge: Symposium Report. Discovery Science 2001: 45-49 | |
| 59 | Ljupco Todorovski, Saso Dzeroski: Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery. ECML 2001: 478-490 | |
| 58 | 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 | |
| 57 | 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) | |
| 56 | Saso Dzeroski, Luc De Raedt, Kurt Driessens: Relational Reinforcement Learning. Machine Learning 43(1/2): 7-52 (2001) | |
| 55 | Peter A. Flach, Saso Dzeroski: Editorial: Inductive Logic Programming is Coming of Age. Machine Learning 44(3): 207-209 (2001) | |
| 2000 | ||
| 54 | James Cussens, Saso Dzeroski: Learning Language in Logic Springer 2000 | |
| 53 | Ljupco Todorovski, Saso Dzeroski, Ashwin Srinivasan, Jonathan Whiteley, David Gavaghan: Discovering the Structure of Partial Differential Equations from Example Behaviour. ICML 2000: 991-998 | |
| 52 | Dimitar Hristovski, Saso Dzeroski, Borut Peterlin, Anamarija Rozic-Hristovski: Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases. PKDD 2000: 446-451 | |
| 51 | Ljupco Todorovski, Saso Dzeroski: Combining Multiple Models with Meta Decision Trees. PKDD 2000: 54-64 | |
| 50 | Saso Dzeroski, Damjan Demsar, Jasna Grbovic: Predicting Chemical Parameters of River Water Quality from Bioindicator Data. Appl. Intell. 13(1): 7-17 (2000) | |
| 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) | |
| 1999 | ||
| 48 | Ivan Bratko, Saso Dzeroski: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999 Morgan Kaufmann 1999 | |
| 47 | Saso Dzeroski, Peter A. Flach: Inductive Logic Programming, 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings Springer 1999 | |
| 46 | James Cussens, Saso Dzeroski, Tomaz Erjavec: Morphosyntactic Tagging of Slovene Using Progol. ILP 1999: 68-79 | |
| 45 | Saso Dzeroski, Hendrik Blockeel, Boris Kompare, Stefan Kramer, Bernhard Pfahringer, Wim Van Laer: Experiments in Predicting Biodegradability. ILP 1999: 80-91 | |
| 44 | Saso Dzeroski, James Cussens, Suresh Manandhar: An Introduction to Inductive Logic Programming and Learning Language in Logic. Learning Language in Logic 1999: 3-35 | |
| 43 | Saso Dzeroski, Tomaz Erjavec: Learning to Lemmatise Slovene Words. Learning Language in Logic 1999: 69-88 | |
| 42 | Hendrik Blockeel, Saso Dzeroski, Jasna Grbovic: Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE. PKDD 1999: 32-40 | |
| 41 | Ljupco Todorovski, Saso Dzeroski: Experiments in Meta-level Learning with ILP. PKDD 1999: 98-106 | |
| 40 | Saso Dzeroski, Nada Lavrac: Editorial. Data Min. Knowl. Discov. 3(1): 5-6 (1999) | |
| 39 | Nada Lavrac, Saso Dzeroski, Masayuki Numao: Inductive Logic Programming for Relational Knowledge Discovery. New Generation Comput. 17(1): 3-23 (1999) | |
| 1998 | ||
| 38 | Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure: ILP Experiments in Detecting Traffic Problems. ECML 1998: 61-66 | |
| 37 | Saso Dzeroski, Luc De Raedt, Hendrik Blockeel: Relational Reinforcement Learning. ICML 1998: 136-143 | |
| 36 | Saso Dzeroski, Luc De Raedt, Hendrik Blockeel: Relational Reinforcement Learning. ILP 1998: 11-22 | |
| 35 | Suresh Manandhar, Saso Dzeroski, Tomaz Erjavec: Learning Multilingual Morphology with CLOG. ILP 1998: 135-144 | |
| 34 | Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure, Stephen Muggleton, Wim Van Laer: Detecting Traffic Problems with ILP. ILP 1998: 281-290 | |
| 33 | 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) | |
| 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) | |
| 1997 | ||
| 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, George 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 | |
| 1996 | ||
| 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, Steffen Schulze-Kremer, Karsten R. Heidtke, Karsten Siems, Dietrich Wettschereck: Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra. Inductive Logic Programming Workshop 1996: 41-54 | |
| 22 | Saso Dzeroski: Inductive Logic Programming and Knowledge Discovery in Databases. Advances in Knowledge Discovery and Data Mining 1996: 117-152 | |
| 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 Commun. 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) | |
| 1995 | ||
| 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. J. Intell. Inf. Syst. 4(1): 89-108 (1995) | |
| 15 | Ivan Bratko, Saso Dzeroski: Engineering Applications of ILP. New Generation Comput. 13(3&4): 313-333 (1995) | |
| 1994 | ||
| 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. Artif. Intell. 70(1-2): 375-392 (1994) | |
| 12 | Jörg-Uwe Kietz, Saso Dzeroski: Inductive Logic Programming and Learnability. SIGART Bulletin 5(1): 22-32 (1994) | |
| 1993 | ||
| 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. IEEE Trans. Knowl. Data Eng. 5(6): 939-949 (1993) | |
| 1992 | ||
| 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 | |
| 1991 | ||
| 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 | |