| 2008 |
|---|
| 103 | | Filip Zelezný,
Nada Lavrac:
Inductive Logic Programming, 18th International Conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008, Proceedings
Springer 2008 |
| 102 | EE | Jeroen S. de Bruin,
Joost N. Kok,
Nada Lavrac,
Igor Trajkovski:
On the Design of Knowledge Discovery Services Design Patterns and Their Application in a Use Case Implementation.
ISoLA 2008: 649-662 |
| 101 | EE | Blaz Fortuna,
Nada Lavrac,
Paola Velardi:
Advancing Topic Ontology Learning through Term Extraction.
PRICAI 2008: 626-635 |
| 100 | EE | Dragan Gamberger,
Nada Lavrac,
Johannes Fürnkranz:
Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach.
PRICAI 2008: 636-645 |
| 99 | EE | Joël Plisson,
Nada Lavrac,
Dunja Mladenic,
Tomaz Erjavec:
Ripple Down Rule learning for automated word lemmatisation.
AI Commun. 21(1): 15-26 (2008) |
| 98 | EE | Igor Trajkovski,
Filip Zelezný,
Nada Lavrac,
Jakub Tolar:
Learning Relational Descriptions of Differentially Expressed Gene Groups.
IEEE Transactions on Systems, Man, and Cybernetics, Part C 38(1): 16-25 (2008) |
| 97 | EE | Igor Trajkovski,
Nada Lavrac,
Jakub Tolar:
SEGS: Search for enriched gene sets in microarray data.
Journal of Biomedical Informatics 41(4): 588-601 (2008) |
| 2007 |
|---|
| 96 | | Michael R. Berthold,
John Shawe-Taylor,
Nada Lavrac:
Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings
Springer 2007 |
| 95 | EE | Petra Kralj,
Nada Lavrac,
Dragan Gamberger,
Antonija Krstacic:
Contrast Set Mining for Distinguishing Between Similar Diseases.
AIME 2007: 109-118 |
| 94 | EE | Igor Trajkovski,
Nada Lavrac:
Interpreting Gene Expression Data by Searching for Enriched Gene Sets.
AIME 2007: 144-148 |
| 93 | EE | Dragan Gamberger,
Nada Lavrac:
Supporting Factors in Descriptive Analysis of Brain Ischaemia.
AIME 2007: 155-159 |
| 92 | EE | Aleksander Pur,
Marko Bohanec,
Nada Lavrac,
Bojan Cestnik,
Marko Debeljak,
Anton Gradisek:
Monitoring Human Resources of a Public Health-Care System Through Intelligent Data Analysis and Visualization.
AIME 2007: 175-179 |
| 91 | EE | Igor Trajkovski,
Nada Lavrac:
Efficient Generation of Biologically Relevant Enriched Gene Sets.
ISBRA 2007: 248-259 |
| 90 | EE | Petra Kralj,
Nada Lavrac,
Dragan Gamberger,
Antonija Krstacic:
Contrast Set Mining Through Subgroup Discovery Applied to Brain Ischaemina Data.
PAKDD 2007: 579-586 |
| 89 | EE | Damjan Demsar,
Igor Mozetic,
Nada Lavrac:
Collaboration Opportunity Finder.
Virtual Enterprises and Collaborative Networks 2007: 179-186 |
| 88 | EE | Dragan Gamberger,
Nada Lavrac,
Antonija Krstacic,
Goran Krstacic:
Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis.
Appl. Intell. 27(3): 205-217 (2007) |
| 87 | EE | Nada Lavrac,
Peter Ljubic,
Tanja Urbani,
Gregor Papa,
Mitja Jermol,
S. Bollhalter:
Trust Modeling for Networked Organizations Using Reputation and Collaboration Estimates.
IEEE Transactions on Systems, Man, and Cybernetics, Part C 37(3): 429-439 (2007) |
| 86 | EE | Joël Plisson,
Peter Ljubic,
Igor Mozetic,
Nada Lavrac:
An Ontology for Virtual Organization Breeding Environments.
IEEE Transactions on Systems, Man, and Cybernetics, Part C 37(6): 1327-1341 (2007) |
| 85 | EE | Nada Lavrac,
Marko Bohanec,
Aleksander Pur,
Bojan Cestnik,
Marko Debeljak,
Andrej Kobler:
Data mining and visualization for decision support and modeling of public health-care resources.
Journal of Biomedical Informatics 40(4): 438-447 (2007) |
| 2006 |
|---|
| 84 | | Ljupco Todorovski,
Nada Lavrac,
Klaus P. Jantke:
Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings
Springer 2006 |
| 83 | EE | Igor Trajkovski,
Filip Zelezný,
Jakub Tolar,
Nada Lavrac:
Relational Subgroup Discovery for Descriptive Analysis of Microarray Data.
CompLife 2006: 86-96 |
| 82 | EE | Monika Záková,
Filip Zelezný,
Javier A. Garcia-Sedano,
Cyril Masia Tissot,
Nada Lavrac,
Petr Kremen,
Javier Molina:
Relational Data Mining Applied to Virtual Engineering of Product Designs.
ILP 2006: 439-453 |
| 81 | EE | Gemma C. Garriga,
Petra Kralj,
Nada Lavrac:
Closed Sets for Labeled Data.
PKDD 2006: 163-174 |
| 80 | | Igor Trajkovski,
Filip Zelezný,
Nada Lavrac,
Jakub Tolar:
Relational Descriptive Analysis of Gene Expression Data.
STAIRS 2006: 184-195 |
| 79 | EE | Branko Kavsek,
Nada Lavrac:
APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery.
Applied Artificial Intelligence 20(7): 543-583 (2006) |
| 78 | EE | Filip Zelezný,
Nada Lavrac:
Propositionalization-based relational subgroup discovery with RSD.
Machine Learning 62(1-2): 33-63 (2006) |
| 2005 |
|---|
| 77 | EE | Nada Lavrac,
Marko Bohanec,
Aleksander Pur,
Bojan Cestnik,
Mitja Jermol,
Tanja Urbancic,
Marko Debeljak,
Branko Kavsek,
Tadeja Kopac:
Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies.
AIME 2005: 414-418 |
| 76 | EE | Aleksander Pur,
Marko Bohanec,
Bojan Cestnik,
Nada Lavrac,
Marko Debeljak,
Tadeja Kopac:
Data Mining for Decision Support: An Application in Public Health Care.
IEA/AIE 2005: 459-469 |
| 75 | EE | Nada Lavrac,
Peter Ljubic,
Mitja Jermol,
Gregor Papa:
A Decision Support Approach to Modeling Trust in Networked Organizations.
IEA/AIE 2005: 746-748 |
| 74 | EE | Nada Lavrac:
Subgroup Discovery Techniques and Applications.
PAKDD 2005: 2-14 |
| 73 | | Nada Lavrac,
Blaz Zupan:
Data Mining in Medicine.
The Data Mining and Knowledge Discovery Handbook 2005: 1107-1138 |
| 2004 |
|---|
| 72 | EE | Nada Lavrac,
Dragan Gamberger:
Relevancy in Constraint-Based Subgroup Discovery.
Constraint-Based Mining and Inductive Databases 2004: 243-266 |
| 71 | | Dragan Gamberger,
Nada Lavrac:
Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics.
ECAI 2004: 470-474 |
| 70 | EE | Nada Lavrac,
Filip Zelezný,
Saso Dzeroski:
Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery.
Local Pattern Detection 2004: 71-88 |
| 69 | | Branko Kavsek,
Nada Lavrac,
Ljupco Todorovski:
ROC Analysis of Example Weighting in Subgroup Discovery.
ROCAI 2004: 55-60 |
| 68 | | Mitja Jermol,
Nada Lavrac,
Tanja Urbancic,
Tadeja Kopac:
Supporting a Public Health Care Virtual Organization by Knowledge Technologies.
Virtual Enterprises and Collaborative Networks 2004: 567-576 |
| 67 | EE | Dragan Gamberger,
Nada Lavrac,
Filip Zelezný,
Jakub Tolar:
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.
Journal of Biomedical Informatics 37(4): 269-284 (2004) |
| 66 | EE | Nada Lavrac,
Branko Kavsek,
Peter A. Flach,
Ljupco Todorovski:
Subgroup Discovery with CN2-SD.
Journal of Machine Learning Research 5: 153-188 (2004) |
| 65 | EE | Nada Lavrac,
Bojan Cestnik,
Dragan Gamberger,
Peter A. Flach:
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned.
Machine Learning 57(1-2): 115-143 (2004) |
| 64 | EE | Nada Lavrac,
Hiroshi Motoda,
Tom Fawcett,
Robert Holte,
Pat Langley,
Pieter W. Adriaans:
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving.
Machine Learning 57(1-2): 13-34 (2004) |
| 63 | EE | Nada Lavrac,
Hiroshi Motoda,
Tom Fawcett:
Editorial: Data Mining Lessons Learned.
Machine Learning 57(1-2): 5-11 (2004) |
| 2003 |
|---|
| 62 | | Nada Lavrac,
Dragan Gamberger,
Ljupco Todorovski,
Hendrik Blockeel:
Machine Learning: ECML 2003, 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings
Springer 2003 |
| 61 | | Nada Lavrac,
Dragan Gamberger,
Hendrik Blockeel,
Ljupco Todorovski:
Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings
Springer 2003 |
| 60 | EE | Dragan Gamberger,
Nada Lavrac:
Analysis of Gene Expression Data by the Logic Minimization Approach.
AIME 2003: 244-248 |
| 59 | EE | Branko Kavsek,
Nada Lavrac,
Viktor Jovanoski:
APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery.
IDA 2003: 230-241 |
| 58 | EE | Mark-A. Krogel,
Simon Rawles,
Filip Zelezný,
Peter A. Flach,
Nada Lavrac,
Stefan Wrobel:
Comparative Evaluation of Approaches to Propositionalization.
ILP 2003: 197-214 |
| 57 | EE | Dragan Gamberger,
Nada Lavrac:
Active subgroup mining: a case study in coronary heart disease risk group detection.
Artificial Intelligence in Medicine 28(1): 27-57 (2003) |
| 56 | EE | Mitja Jermol,
Nada Lavrac,
Tanja Urbancic:
Managing business intelligence in a virtual enterprise: A case study and knowledge management lessons learned.
Journal of Intelligent and Fuzzy Systems 14(3): 121-136 (2003) |
| 2002 |
|---|
| 55 | EE | Peter A. Flach,
Nada Lavrac:
Learning in Clausal Logic: A Perspective on Inductive Logic Programming.
Computational Logic: Logic Programming and Beyond 2002: 437-471 |
| 54 | EE | Nada Lavrac,
Peter A. Flach,
Branko Kavsek,
Ljupco Todorovski:
Adapting classification rule induction to subgroup discovery.
ICDM 2002: 266-273 |
| 53 | | Dragan Gamberger,
Nada Lavrac:
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain.
ICML 2002: 163-170 |
| 52 | EE | Nada Lavrac,
Filip Zelezný,
Peter A. Flach:
RSD: Relational Subgroup Discovery through First-Order Feature Construction.
ILP 2002: 149-165 |
| 51 | EE | Dragan Gamberger,
Nada Lavrac:
Generating Actionable Knowledge by Expert-Guided Subgroup Discovery.
PKDD 2002: 163-174 |
| 50 | | Nada Lavrac:
Virtual Enterprise for Data Mining and Decision Support.
PRO-VE 2002 |
| 49 | EE | Dragan Gamberger,
Nada Lavrac:
Expert-Guided Subgroup Discovery: Methodology and Application.
J. Artif. Intell. Res. (JAIR) 17: 501-527 (2002) |
| 48 | EE | Dragan Gamberger,
Nada Lavrac,
Goran Krstacic:
Confirmation rule induction and its applications to coronary heart disease diagnosis and risk group discovery.
Journal of Intelligent and Fuzzy Systems 12(1): 35-48 (2002) |
| 2001 |
|---|
| 47 | EE | Branko Kavsek,
Nada Lavrac,
Anuska Ferligoj:
Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction.
ECML 2001: 251-262 |
| 46 | EE | Viktor Jovanoski,
Nada Lavrac:
Classification Rule Learning with APRIORI-C.
EPIA 2001: 44-51 |
| 45 | EE | Nada Lavrac,
Peter A. Flach:
An extended transformation approach to inductive logic programming.
ACM Trans. Comput. Log. 2(4): 458-494 (2001) |
| 44 | | Elpida T. Keravnou,
Nada Lavrac:
AIM portraits: tracing the evolution of artificial intelligence in medicine and predicting its future in the new millennium.
Artificial Intelligence in Medicine 23(1): 1-4 (2001) |
| 2000 |
|---|
| 43 | EE | Dragan Gamberger,
Nada Lavrac,
Goran Krstacic,
Tomislav Smuc:
Inconsistency Tests for Patient Records in a Coronary Heart Disease Database.
ISMDA 2000: 183-189 |
| 42 | EE | Ljupco Todorovski,
Peter A. Flach,
Nada Lavrac:
Predictive Performance of Weghted Relative Accuracy.
PKDD 2000: 255-264 |
| 41 | EE | Dragan Gamberger,
Nada Lavrac:
Confirmation Rule Sets.
PKDD 2000: 34-43 |
| 40 | | 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 |
|---|
| 39 | EE | Dragan Gamberger,
Nada Lavrac,
Ciril Groselj:
Diagnostic Rules of Increased Reliability for Critical Medical Applications.
AIMDM 1999: 361-365 |
| 38 | EE | Nada Lavrac:
Machine Learning for Data Mining in Medicine.
AIMDM 1999: 47-64 |
| 37 | | Nada Lavrac:
Challenges for Inductive Logic Programming.
EPIA 1999: 16-33 |
| 36 | | Dragan Gamberger,
Nada Lavrac,
Ciril Groselj:
Experiments with Noise Filtering in a Medical Domain.
ICML 1999: 143-151 |
| 35 | EE | Nada Lavrac,
Peter A. Flach,
Blaz Zupan:
Rule Evaluation Measures: A Unifying View.
ILP 1999: 174-185 |
| 34 | | Nada Lavrac:
Selected techniques for data mining in medicine.
Artificial Intelligence in Medicine 16(1): 3-23 (1999) |
| 33 | | Saso Dzeroski,
Nada Lavrac:
Editorial.
Data Min. Knowl. Discov. 3(1): 5-6 (1999) |
| 32 | | Nada Lavrac,
Dragan Gamberger,
Viktor Jovanoski:
A Study of Relevance for Learning in Deductive Databases.
J. Log. Program. 40(2-3): 215-249 (1999) |
| 31 | | Nada Lavrac,
Saso Dzeroski,
Masayuki Numao:
Inductive Logic Programming for Relational Knowledge Discovery.
New Generation Comput. 17(1): 3-23 (1999) |
| 1998 |
|---|
| 30 | | Nada Lavrac:
Inductive Logic Programming for Relational Knowledge Discovery.
IJCSLP 1998: 7-24 |
| 29 | | Nada Lavrac,
Blaz Zupan,
Igor Kononenko,
Matjaz Kukar,
Elpida T. Keravnou:
Intelligent Data Analysis for Medical Diagnosis: Using Machine Learning and Temporal Abstraction.
AI Commun. 11(3-4): 191-218 (1998) |
| 28 | | Nada Lavrac,
Dragan Gamberger,
Peter D. Turney:
A Relevancy Filter for Constructive Induction.
IEEE Intelligent Systems 13(2): 50-56 (1998) |
| 1997 |
|---|
| 27 | | Nada Lavrac,
Saso Dzeroski:
Inductive Logic Programming, 7th International Workshop, ILP-97, Prague, Czech Republic, September 17-20, 1997, Proceedings
Springer 1997 |
| 26 | | Igor Zelic,
Igor Kononenko,
Nada Lavrac,
Vanja Vuga:
Machine Learning Applied to Diagnosis of Sport Injuries.
AIME 1997: 138-141 |
| 25 | EE | Igor Zelic,
Igor Kononenko,
Nada Lavrac,
Vanja Vuga:
Diagnosis of sport injuries with machine learning: explanation of induced decisions.
CBMS 1997: 195-199 |
| 24 | EE | Iztok A. Pilih,
Dunja Mladenic,
Nada Lavrac,
Tine S. Prevec:
Using machine learning for outcome prediction of patients with severe head injury.
CBMS 1997: 200-204 |
| 23 | | Dragan Gamberger,
Nada Lavrac:
Conditions for Occam's Razor Applicability and Noise Elimination.
ECML 1997: 108-123 |
| 22 | EE | Darko Zupanic,
Milan Hodoscek,
Nada Lavrac,
Igor Mozetic:
Global Energy Minimization of Small Molecules Combining Constraint Logic Programming and Molecular Mechanics.
Journal of Chemical Information and Computer Sciences 37(6): 966-970 (1997) |
| 1996 |
|---|
| 21 | | Nada Lavrac,
Dragan Gamberger,
Peter D. Turney:
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm.
ALT 1996: 127-134 |
| 20 | | Dragan Gamberger,
Nada Lavrac,
Saso Dzeroski:
Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois.
ALT 1996: 199-212 |
| 19 | | Dragan Gamberger,
Nada Lavrac:
Noise Detection and Elimination Applied to Noise Handling in a KRK Chess Endgame.
Inductive Logic Programming Workshop 1996: 72-88 |
| 18 | | 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) |
| 17 | | Nada Lavrac,
Stefan Wrobel:
Induktive Logikprogrammierung - Grundlagen und Techniken.
KI 10(3): 46-54 (1996) |
| 16 | EE | Luc De Raedt,
Nada Lavrac:
Multiple Predicate Learning in Two Inductive Logic Programming Settings.
Logic Journal of the IGPL 4(2): 227-254 (1996) |
| 15 | | 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 |
|---|
| 14 | | Nada Lavrac,
Stefan Wrobel:
Machine Learning: ECML-95, 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25-27, 1995, Proceedings
Springer 1995 |
| 13 | | Nada Lavrac,
Luc De Raedt:
Inductive Logic Programming: A Survey of European Research.
AI Commun. 8(1): 3-19 (1995) |
| 1994 |
|---|
| 12 | | Nada Lavrac:
Inductive Logic Programming.
WLP 1994: 146-160 |
| 1993 |
|---|
| 11 | | Luc De Raedt,
Nada Lavrac,
Saso Dzeroski:
Multiple Predicate Learning.
IJCAI 1993: 1037-1043 |
| 10 | | Luc De Raedt,
Nada Lavrac:
The Many Faces of Inductive Logic Programming.
ISMIS 1993: 435-449 |
| 9 | | 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) |
| 8 | EE | Saso Dzeroski,
Nada Lavrac:
Inductive Learning in Deductive Databases.
IEEE Trans. Knowl. Data Eng. 5(6): 939-949 (1993) |
| 1992 |
|---|
| 7 | | Nada Lavrac,
Saso Dzeroski:
Background Knowledge and Declarative Bias in Inductive Concept Learning.
AII 1992: 51-71 |
| 6 | | Matevz Kovacic,
Nada Lavrac,
Marko Grobelnik,
Darko Zupanic,
Dunja Mladenic:
Stochastic Search in Inductive Logic Programming.
ECAI 1992: 444-445 |
| 1991 |
|---|
| 5 | | Nada Lavrac,
Saso Dzeroski,
Marko Grobelnik:
Learning Nonrecursive Definitions of Relations with LINUS.
EWSL 1991: 265-281 |
| 4 | | Saso Dzeroski,
Nada Lavrac:
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL.
ML 1991: 399-402 |
| 3 | | Nada Lavrac,
Saso Dzeroski,
Vladimir Pirnat,
Viljem Krizman:
Learning Rules for Early Diagnosis of Rheumatic Diseases.
SCAI 1991: 138-149 |
| 1987 |
|---|
| 2 | | Matjaz Gams,
Nada Lavrac:
Review of Five Empirical Learning Systems Within a Proposed Schemata.
EWSL 1987: 46-66 |
| 1986 |
|---|
| 1 | | Ryszard S. Michalski,
Igor Mozetic,
Jiarong Hong,
Nada Lavrac:
The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains.
AAAI 1986: 1041-1047 |
