| 2008 |
|---|
| 120 | EE | Pance Panov,
Saso Dzeroski,
Larisa N. Soldatova:
OntoDM: An Ontology of Data Mining.
ICDM Workshops 2008: 752-760 |
| 119 | EE | Aleksandar Peckov,
Saso Dzeroski,
Ljupco Todorovski:
A Minimal Description Length Scheme for Polynomial Regression.
PAKDD 2008: 284-295 |
| 118 | EE | Bernard Zenko,
Saso Dzeroski:
Learning Classification Rules for Multiple Target Attributes.
PAKDD 2008: 454-465 |
| 117 | EE | Will Bridewell,
Pat Langley,
Ljupco Todorovski,
Saso Dzeroski:
Inductive process modeling.
Machine Learning 71(1): 1-32 (2008) |
| 116 | EE | 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 |
|---|
| 115 | | Saso Dzeroski,
Ljupco Todorovski:
Computational Discovery of Scientific Knowledge, Introduction, Techniques, and Applications in Environmental and Life Sciences
Springer 2007 |
| 114 | | 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 |
| 113 | EE | Saso Dzeroski,
Pat Langley,
Ljupco Todorovski:
Computational Discovery of Scientific Knowledge.
Computational Discovery of Scientific Knowledge 2007: 1-14 |
| 112 | EE | 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 |
| 111 | EE | Ljupco Todorovski,
Saso Dzeroski:
Integrating Domain Knowledge in Equation Discovery.
Computational Discovery of Scientific Knowledge 2007: 69-97 |
| 110 | EE | Jan Struyf,
Saso Dzeroski:
Clustering Trees with Instance Level Constraints.
ECML 2007: 359-370 |
| 109 | EE | Annalisa Appice,
Saso Dzeroski:
Stepwise Induction of Multi-target Model Trees.
ECML 2007: 502-509 |
| 108 | EE | Dragi Kocev,
Celine Vens,
Jan Struyf,
Saso Dzeroski:
Ensembles of Multi-Objective Decision Trees.
ECML 2007: 624-631 |
| 107 | EE | Pance Panov,
Saso Dzeroski:
Combining Bagging and Random Subspaces to Create Better Ensembles.
IDA 2007: 118-129 |
| 106 | | Annalisa Appice,
Saso Dzeroski:
Inducing Multi-Target Model Trees in a Stepwise Fashion.
SEBD 2007: 16-27 |
| 105 | EE | 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 | EE | Taneli Mielikäinen,
Pance Panov,
Saso Dzeroski:
Itemset Support Queries Using Frequent Itemsets and Their Condensed Representations.
Discovery Science 2006: 161-172 |
| 103 | EE | Saso Dzeroski:
From Inductive Logic Programming to Relational Data Mining.
JELIA 2006: 1-14 |
| 102 | EE | Dragi Kocev,
Jan Struyf,
Saso Dzeroski:
Beam Search Induction and Similarity Constraints for Predictive Clustering Trees.
KDID 2006: 134-151 |
| 101 | EE | Saso Dzeroski:
Towards a General Framework for Data Mining.
KDID 2006: 259-300 |
| 100 | EE | Saso Dzeroski,
Valentin Gjorgjioski,
Ivica Slavkov,
Jan Struyf:
Analysis of Time Series Data with Predictive Clustering Trees.
KDID 2006: 63-80 |
| 99 | EE | 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 | EE | 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 | EE | Jan Struyf,
Saso Dzeroski,
Hendrik Blockeel,
Amanda Clare:
Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics.
EPIA 2005: 272-283 |
| 95 | EE | Kurt Driessens,
Saso Dzeroski:
Combining model-based and instance-based learning for first order regression.
ICML 2005: 193-200 |
| 94 | EE | Jan Struyf,
Saso Dzeroski:
Constraint Based Induction of Multi-objective Regression Trees.
KDID 2005: 222-233 |
| 93 | EE | 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 | EE | Hendrik Blockeel,
Saso Dzeroski:
Multi-Relational Data Mining 2005: workshop report.
SIGKDD Explorations 7(2): 126-128 (2005) |
| 2004 |
|---|
| 90 | EE | Saso Dzeroski,
Ljupco Todorovski,
Peter Ljubic:
Inductive Queries on Polynomial Equations.
Constraint-Based Mining and Inductive Databases 2004: 127-154 |
| 89 | EE | Saso Dzeroski,
Ljupco Todorovski,
Peter Ljubic:
Inductive Databases of Polynomial Equations.
DaWaK 2004: 159-168 |
| 88 | EE | Ljupco Todorovski,
Peter Ljubic,
Saso Dzeroski:
Inducing Polynomial Equations for Regression.
ECML 2004: 441-452 |
| 87 | EE | Celine Vens,
Anneleen Van Assche,
Hendrik Blockeel,
Saso Dzeroski:
First Order Random Forests with Complex Aggregates.
ILP 2004: 323-340 |
| 86 | EE | Nada Lavrac,
Filip Zelezný,
Saso Dzeroski:
Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery.
Local Pattern Detection 2004: 71-88 |
| 85 | EE | Tomaz Erjavec,
Saso Dzeroski:
Machine Learning of Morphosyntactic Structure: Lemmatizing Unknown Slovene Words.
Applied Artificial Intelligence 18(1): 17-41 (2004) |
| 84 | EE | 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 | EE | Saso Dzeroski,
Bernard Zenko:
Is Combining Classifiers with Stacking Better than Selecting the Best One?
Machine Learning 54(3): 255-273 (2004) |
| 82 | EE | Kurt Driessens,
Saso Dzeroski:
Integrating Guidance into Relational Reinforcement Learning.
Machine Learning 57(3): 271-304 (2004) |
| 81 | EE | Saso Dzeroski,
Hendrik Blockeel:
Multi-relational data mining 2004: workshop report.
SIGKDD Explorations 6(2): 140-141 (2004) |
| 80 | EE | 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 | EE | Saso Dzeroski,
Ljupco Todorovski,
Peter Ljubic:
Using Constraints in Discovering Dynamics.
Discovery Science 2003: 297-305 |
| 77 | EE | Saso Dzeroski,
Ljupco Todorovski,
Boris Zmazek,
Janja Vaupotic,
Ivan Kobal:
Modelling Soil Radon Concentration for Earthquake Prediction.
Discovery Science 2003: 87-99 |
| 76 | EE | 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 | EE | Saso Dzeroski:
Multi-relational data mining: an introduction.
SIGKDD Explorations 5(1): 1-16 (2003) |
| 72 | EE | Saso Dzeroski,
Luc De Raedt:
Multi-relational data mining: the current frontiers.
SIGKDD Explorations 5(1): 100-101 (2003) |
| 71 | EE | Saso Dzeroski,
Luc De Raedt,
Stefan Wrobel:
Multirelational data mining 2003: workshop report.
SIGKDD Explorations 5(2): 200-202 (2003) |
| 2002 |
|---|
| 70 | EE | Saso Dzeroski:
Relational Reinforcement Learning for Agents in Worlds with Objects.
Adaptive Agents and Multi-Agents Systems 2002: 306-322 |
| 69 | EE | Ljupco Todorovski,
Hendrik Blockeel,
Saso Dzeroski:
Ranking with Predictive Clustering Trees.
ECML 2002: 444-455 |
| 68 | EE | 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 | EE | Saso Dzeroski:
Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery.
ILP 2002: 346-349 |
| 63 | EE | Saso Dzeroski,
Bernard Zenko:
Stacking with Multi-response Model Trees.
Multiple Classifier Systems 2002: 201-211 |
| 62 | EE | Saso Dzeroski,
Luc De Raedt:
Multi-Relational Data Mining: a Workshop Report.
SIGKDD Explorations 4(2): 122-124 (2002) |
| 2001 |
|---|
| 61 | EE | Ljupco Todorovski,
Saso Dzeroski:
Theory Revision in Equation Discovery.
Discovery Science 2001: 389-400 |
| 60 | EE | Saso Dzeroski,
Pat Langley:
Computational Discovery of Communicable Knowledge: Symposium Report.
Discovery Science 2001: 45-49 |
| 59 | EE | Ljupco Todorovski,
Saso Dzeroski:
Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery.
ECML 2001: 478-490 |
| 58 | EE | 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 | EE | 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 | EE | 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 | EE | James Cussens,
Saso Dzeroski,
Tomaz Erjavec:
Morphosyntactic Tagging of Slovene Using Progol.
ILP 1999: 68-79 |
| 45 | EE | Saso Dzeroski,
Hendrik Blockeel,
Boris Kompare,
Stefan Kramer,
Bernhard Pfahringer,
Wim Van Laer:
Experiments in Predicting Biodegradability.
ILP 1999: 80-91 |
| 44 | EE | 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 | EE | 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 | EE | 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 | EE | 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 | EE | 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 |
