Nada Lavrac

	2012
130		Ingrid Petric, Bojan Cestnik, Nada Lavrac, Tanja Urbancic: Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining. Comput. J. 55(1): 47-61 (2012)
129		Vid Podpecan, Monika Zemenova, Nada Lavrac: Orange4WS Environment for Service-Oriented Data Mining. Comput. J. 55(1): 82-98 (2012)
	2011
128		Mor Peleg, Nada Lavrac, Carlo Combi: Artificial Intelligence in Medicine - 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings Springer 2011
127		Borut Sluban, Matjaz Jursic, Bojan Cestnik, Nada Lavrac: Evaluating Outliers for Cross-Context Link Discovery. AIME 2011: 343-347
126		Miha Grcar, Nada Lavrac: A Methodology for Mining Document-Enriched Heterogeneous Information Networks. Discovery Science 2011: 107-121
125		Nada Lavrac, Anze Vavpetic, Larisa N. Soldatova, Igor Trajkovski, Petra Kralj Novak: Using Ontologies in Semantic Data Mining with SEGS and g-SEGS. Discovery Science 2011: 165-178
124		Vid Podpecan, Nada Lavrac, Igor Mozetic, Petra Kralj Novak, Igor Trajkovski, Laura Langohr, Kimmo Kulovesi, Hannu Toivonen, Marko Petek, Helena Motaln, Kristina Gruden: SegMine workflows for semantic microarray data analysis in Orange4WS. BMC Bioinformatics 12: 416 (2011)
123		Dragan Gamberger, Nada Lavrac: Expert-Guided Subgroup Discovery: Methodology and Application CoRR abs/1106.4576: (2011)
122		Monika Záková, Petr Kremen, Filip Zelezný, Nada Lavrac: Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning. IEEE T. Automation Science and Engineering 8(2): 253-264 (2011)
	2010
121		Miha Grcar, Vid Podpecan, Matjaz Jursic, Nada Lavrac: Efficient Visualization of Document Streams. Discovery Science 2010: 174-188
120		Borut Sluban, Dragan Gamberger, Nada Lavrac: Advances in Class Noise Detection. ECAI 2010: 1105-1106
119		Vid Podpecan, Monika Záková, Nada Lavrac: Workflow Construction for Service-Oriented Knowledge Discovery. ISoLA (1) 2010: 313-327
118		Vid Podpecan, Miha Grcar, Nada Lavrac: Semi-supervised Constrained Clustering: An Expert-Guided Data Analysis Methodology. PRICAI 2010: 219-230
117		Borut Sluban, Dragan Gamberger, Nada Lavrac: Performance Analysis of Class Noise Detection Algorithms. STAIRS 2010: 303-314
116		Nada Lavrac, Johannes Fürnkranz, Dragan Gamberger: Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. Advances in Machine Learning I 2010: 121-146
115		Nada Lavrac, Blaz Zupan: Data Mining in Medicine. Data Mining and Knowledge Discovery Handbook 2010: 1111-1136
114		Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb: Supervised Descriptive Rule Induction. Encyclopedia of Machine Learning 2010: 938-941
113		Matjaz Jursic, Igor Mozetic, Tomaz Erjavec, Nada Lavrac: LemmaGen: Multilingual Lemmatisation with Induced Ripple-Down Rules. J. UCS 16(9): 1190-1214 (2010)
	2009
112		Petra Kralj Novak, Nada Lavrac, Dragan Gamberger, Antonija Krstacic: CSM-SD: Methodology for contrast set mining through subgroup discovery. Journal of Biomedical Informatics 42(1): 113-122 (2009)
111		Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb: Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. Journal of Machine Learning Research 10: 377-403 (2009)
110		Filip Zelezný, Nada Lavrac: Guest editors' introduction: Special issue on Inductive Logic Programming (ILP-2008). Machine Learning 76(1): 1-2 (2009)
	2008
109		Filip Zelezný, Nada Lavrac: Inductive Logic Programming, 18th International Conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008, Proceedings Springer 2008
108		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
107		Blaz Fortuna, Nada Lavrac, Paola Velardi: Advancing Topic Ontology Learning through Term Extraction. PRICAI 2008: 626-635
106		Dragan Gamberger, Nada Lavrac, Johannes Fürnkranz: Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach. PRICAI 2008: 636-645
105		Joël Plisson, Nada Lavrac, Dunja Mladenic, Tomaz Erjavec: Ripple Down Rule learning for automated word lemmatisation. AI Commun. 21(1): 15-26 (2008)
104		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)
103		Igor Trajkovski, Nada Lavrac, Jakub Tolar: SEGS: Search for enriched gene sets in microarray data. Journal of Biomedical Informatics 41(4): 588-601 (2008)
102		Gemma C. Garriga, Petra Kralj, Nada Lavrac: Closed Sets for Labeled Data. Journal of Machine Learning Research 9: 559-580 (2008)
	2007
101		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
100		Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic: Contrast Set Mining for Distinguishing Between Similar Diseases. AIME 2007: 109-118
99		Igor Trajkovski, Nada Lavrac: Interpreting Gene Expression Data by Searching for Enriched Gene Sets. AIME 2007: 144-148
98		Dragan Gamberger, Nada Lavrac: Supporting Factors in Descriptive Analysis of Brain Ischaemia. AIME 2007: 155-159
97		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
96		Igor Trajkovski, Nada Lavrac: Efficient Generation of Biologically Relevant Enriched Gene Sets. ISBRA 2007: 248-259
95		Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic: Contrast Set Mining Through Subgroup Discovery Applied to Brain Ischaemina Data. PAKDD 2007: 579-586
94		Damjan Demsar, Igor Mozetic, Nada Lavrac: Collaboration Opportunity Finder. Virtual Enterprises and Collaborative Networks 2007: 179-186
93		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)
92		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)
91		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)
90		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
89		Ljupco Todorovski, Nada Lavrac, Klaus P. Jantke: Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings Springer 2006
88		Igor Trajkovski, Filip Zelezný, Jakub Tolar, Nada Lavrac: Relational Subgroup Discovery for Descriptive Analysis of Microarray Data. CompLife 2006: 86-96
87		Monika Záková, Filip Zelezný, Javier A. García-Sedano, Cyril Masia Tissot, Nada Lavrac, Petr Kremen, Javier Molina: Relational Data Mining Applied to Virtual Engineering of Product Designs. ILP 2006: 439-453
86		Gemma C. Garriga, Petra Kralj, Nada Lavrac: Closed Sets for Labeled Data. PKDD 2006: 163-174
85		Igor Trajkovski, Filip Zelezný, Nada Lavrac, Jakub Tolar: Relational Descriptive Analysis of Gene Expression Data. STAIRS 2006: 184-195
84		Branko Kavsek, Nada Lavrac: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. Applied Artificial Intelligence 20(7): 543-583 (2006)
83		Filip Zelezný, Nada Lavrac: Propositionalization-based relational subgroup discovery with RSD. Machine Learning 62(1-2): 33-63 (2006)
	2005
82		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
81		Nada Lavrac: SolEuNet: Selected Data Mining Techniques and Applications. GfKl 2005: 32-39
80		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
79		Nada Lavrac, Peter Ljubic, Mitja Jermol, Gregor Papa: A Decision Support Approach to Modeling Trust in Networked Organizations. IEA/AIE 2005: 746-748
78		Nada Lavrac: Subgroup Discovery Techniques and Applications. PAKDD 2005: 2-14
77		Nada Lavrac, Blaz Zupan: Data Mining in Medicine. The Data Mining and Knowledge Discovery Handbook 2005: 1107-1138
	2004
76		Nada Lavrac, Dragan Gamberger: Relevancy in Constraint-Based Subgroup Discovery. Constraint-Based Mining and Inductive Databases 2004: 243-266
75		Dragan Gamberger, Nada Lavrac: Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics. ECAI 2004: 470-474
74		Nada Lavrac, Filip Zelezný, Saso Dzeroski: Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. Local Pattern Detection 2004: 71-88
73		Branko Kavsek, Nada Lavrac, Ljupco Todorovski: ROC Analysis of Example Weighting in Subgroup Discovery. ROCAI 2004: 55-60
72		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
71		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)
70		Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski: Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5: 153-188 (2004)
69		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)
68		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)
67		Nada Lavrac, Hiroshi Motoda, Tom Fawcett: Editorial: Data Mining Lessons Learned. Machine Learning 57(1-2): 5-11 (2004)
	2003
66		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
65		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
64		Dragan Gamberger, Nada Lavrac: Analysis of Gene Expression Data by the Logic Minimization Approach. AIME 2003: 244-248
63		Branko Kavsek, Nada Lavrac, Viktor Jovanoski: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. IDA 2003: 230-241
62		Mark-A. Krogel, Simon Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel: Comparative Evaluation of Approaches to Propositionalization. ILP 2003: 197-214
61		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)
60		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
59		Peter A. Flach, Nada Lavrac: Learning in Clausal Logic: A Perspective on Inductive Logic Programming. Computational Logic: Logic Programming and Beyond 2002: 437-471
58		Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski: Adapting classification rule induction to subgroup discovery. ICDM 2002: 266-273
57		Dragan Gamberger, Nada Lavrac: Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain. ICML 2002: 163-170
56		Nada Lavrac, Filip Zelezný, Peter A. Flach: RSD: Relational Subgroup Discovery through First-Order Feature Construction. ILP 2002: 149-165
55		Dragan Gamberger, Nada Lavrac: Generating Actionable Knowledge by Expert-Guided Subgroup Discovery. PKDD 2002: 163-174
54		Nada Lavrac: Virtual Enterprise for Data Mining and Decision Support. PRO-VE 2002
53		Dragan Gamberger, Nada Lavrac: Expert-Guided Subgroup Discovery: Methodology and Application. J. Artif. Intell. Res. (JAIR) 17: 501-527 (2002)
52		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
51		Branko Kavsek, Nada Lavrac, Anuska Ferligoj: Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction. ECML 2001: 251-262
50		Viktor Jovanoski, Nada Lavrac: Classification Rule Learning with APRIORI-C. EPIA 2001: 44-51
49		Nada Lavrac, Peter A. Flach: An extended transformation approach to inductive logic programming. ACM Trans. Comput. Log. 2(4): 458-494 (2001)
48		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
47		Dragan Gamberger, Nada Lavrac, Goran Krstacic, Tomislav Smuc: Inconsistency Tests for Patient Records in a Coronary Heart Disease Database. ISMDA 2000: 183-189
46		Ljupco Todorovski, Peter A. Flach, Nada Lavrac: Predictive Performance of Weghted Relative Accuracy. PKDD 2000: 255-264
45		Dragan Gamberger, Nada Lavrac: Confirmation Rule Sets. PKDD 2000: 34-43
44		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
43		Dragan Gamberger, Nada Lavrac, Ciril Groselj: Diagnostic Rules of Increased Reliability for Critical Medical Applications. AIMDM 1999: 361-365
42		Nada Lavrac: Machine Learning for Data Mining in Medicine. AIMDM 1999: 47-64
41		Nada Lavrac: Challenges for Inductive Logic Programming. EPIA 1999: 16-33
40		Dragan Gamberger, Nada Lavrac, Ciril Groselj: Experiments with Noise Filtering in a Medical Domain. ICML 1999: 143-151
39		Nada Lavrac, Peter A. Flach, Blaz Zupan: Rule Evaluation Measures: A Unifying View. ILP 1999: 174-185
38		Nada Lavrac: Selected techniques for data mining in medicine. Artificial Intelligence in Medicine 16(1): 3-23 (1999)
37		Saso Dzeroski, Nada Lavrac: Editorial. Data Min. Knowl. Discov. 3(1): 5-6 (1999)
36		Nada Lavrac, Dragan Gamberger, Viktor Jovanoski: A Study of Relevance for Learning in Deductive Databases. J. Log. Program. 40(2-3): 215-249 (1999)
35		Nada Lavrac, Saso Dzeroski, Masayuki Numao: Inductive Logic Programming for Relational Knowledge Discovery. New Generation Comput. 17(1): 3-23 (1999)
	1998
34		Nada Lavrac: Inductive Logic Programming for Relational Knowledge Discovery. IJCSLP 1998: 7-24
33		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)
32		Nada Lavrac, Dragan Gamberger, Peter D. Turney: A Relevancy Filter for Constructive Induction. IEEE Intelligent Systems 13(2): 50-56 (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		Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga: Machine Learning Applied to Diagnosis of Sport Injuries. AIME 1997: 138-141
29		Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga: Diagnosis of sport injuries with machine learning: explanation of induced decisions. CBMS 1997: 195-199
28		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
27		Dragan Gamberger, Nada Lavrac: Conditions for Occam's Razor Applicability and Noise Elimination. ECML 1997: 108-123
26		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
25		Nada Lavrac, Dragan Gamberger, Peter D. Turney: Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm. ALT 1996: 127-134
24		Dragan Gamberger, Nada Lavrac, Saso Dzeroski: Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. ALT 1996: 199-212
23		Dragan Gamberger, Nada Lavrac: Noise Detection and Elimination Applied to Noise Handling in a KRK Chess Endgame. Inductive Logic Programming Workshop 1996: 72-88
22		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)
21		Nada Lavrac, Stefan Wrobel: Induktive Logikprogrammierung - Grundlagen und Techniken. KI 10(3): 46-54 (1996)
20		Luc De Raedt, Nada Lavrac: Multiple Predicate Learning in Two Inductive Logic Programming Settings. Logic Journal of the IGPL 4(2): 227-254 (1996)
19		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
18		Nada Lavrac, Stefan Wrobel: Machine Learning: ECML-95, 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25-27, 1995, Proceedings Springer 1995
17		Nada Lavrac, Luc De Raedt: Inductive Logic Programming: A Survey of European Research. AI Commun. 8(1): 3-19 (1995)
	1994
16		Nada Lavrac, Saso Dzeroski: Inductive logic programming - techniques and applications. Ellis Horwood 1994: I-XIX, 1-293
15		Nada Lavrac: Inductive Logic Programming. WLP 1994: 146-160
14		Nada Lavrac, Saso Dzeroski: Weakening the language bias in LINUS. J. Exp. Theor. Artif. Intell. 6(1): 95-119 (1994)
	1993
13		Luc De Raedt, Nada Lavrac, Saso Dzeroski: Multiple Predicate Learning. IJCAI 1993: 1037-1043
12		Luc De Raedt, Nada Lavrac: The Many Faces of Inductive Logic Programming. ISMIS 1993: 435-449
11		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)
10		Saso Dzeroski, Nada Lavrac: Inductive Learning in Deductive Databases. IEEE Trans. Knowl. Data Eng. 5(6): 939-949 (1993)
	1992
9		Nada Lavrac, Saso Dzeroski: Background Knowledge and Declarative Bias in Inductive Concept Learning. AII 1992: 51-71
8		Matevz Kovacic, Nada Lavrac, Marko Grobelnik, Darko Zupanic, Dunja Mladenic: Stochastic Search in Inductive Logic Programming. ECAI 1992: 444-445
	1991
7		Nada Lavrac, Saso Dzeroski, Marko Grobelnik: Learning Nonrecursive Definitions of Relations with LINUS. EWSL 1991: 265-281
6		Saso Dzeroski, Nada Lavrac: Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. ML 1991: 399-402
5		Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman: Learning Rules for Early Diagnosis of Rheumatic Diseases. SCAI 1991: 138-149
	1989
4		Ivan Bratko, Igor Mozetic, Nada Lavrac: KARDIO - a study in deep and qualitative knowledge for expert systems. MIT Press 1989: I-XIV, 1-260
3		Nada Lavrac, Igor Mozetic: Methods for knowledge acquisition and refinement in second generation expert systems. SIGART Newsletter 108: 63-69 (1989)
	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

Coauthor Index

1	Pieter W. Adriaans	[68]
2	Michael R. Berthold	[101]
3	Hendrik Blockeel	[65] [66]
4	Marko Bohanec	[80] [82] [90] [97]
5	S. Bollhalter	[92]
6	Ivan Bratko	[4]
7	Jeroen S. de Bruin	[108]
8	Bojan Cestnik	[69] [80] [82] [90] [97] [127] [130]
9	Carlo Combi	[128]
10	Marko Debeljak	[80] [82] [90] [97]
11	Damjan Demsar	[94]
12	Saso Dzeroski	[5] [6] [7] [9] [10] [11] [13] [14] [16] [19] [22] [24] [31] [35] [37] [44] [74]
13	Tomaz Erjavec	[105] [113]
14	Tom Fawcett	[67] [68]
15	Anuska Ferligoj	[51]
16	Peter A. Flach	[39] [46] [49] [56] [58] [59] [62] [69] [70]
17	Blaz Fortuna	[107]
18	Johannes Fürnkranz	[106] [116]
19	Dragan Gamberger	[23] [24] [25] [27] [32] [36] [40] [43] [44] [45] [47] [52] [53] [55] [57] [61] [64] [65] [66] [69] [71] [75] [76] [93] [95] [98] [100] [106] [112] [116] [117] [120] [123]
20	Matjaz Gams	[2]
21	Javier A. García-Sedano	[87]
22	Gemma C. Garriga (Gemma Casas-Garriga)	[86] [102]
23	Anton Gradisek	[97]
24	Miha Grcar	[118] [121] [126]
25	Marko Grobelnik	[7] [8]
26	Ciril Groselj	[40] [43]
27	Kristina Gruden	[124]
28	Milan Hodoscek	[26]
29	Robert C. Holte (Robert Holte)	[68]
30	Jiarong Hong	[1]
31	Klaus P. Jantke	[89]
32	Mitja Jermol	[60] [72] [79] [82] [92]
33	Viktor Jovanoski	[36] [50] [63]
34	Matjaz Jursic	[113] [121] [127]
35	Branko Kavsek	[51] [58] [63] [70] [73] [82] [84]
36	Dimitar Kazakov	[22]
37	Elpida T. Keravnou	[33] [48]
38	Andrej Kobler	[90]
39	Joost N. Kok	[108]
40	Igor Kononenko	[29] [30] [33]
41	Tadeja Kopac	[72] [80] [82]
42	Matevz Kovacic	[8]
43	Petr Kremen	[87] [122]
44	Viljem Krizman	[5] [11]
45	Mark-A. Krogel	[62]
46	Antonija Krstacic	[93] [95] [100] [112]
47	Goran Krstacic	[47] [52] [93]
48	Matjaz Kukar	[33]
49	Kimmo Kulovesi	[124]
50	Pat Langley	[68]
51	Laura Langohr	[124]
52	Peter Ljubic	[79] [91] [92]
53	Ryszard S. Michalski	[1]
54	Dunja Mladenic	[8] [28] [105]
55	Javier Molina	[87]
56	Helena Motaln	[124]
57	Hiroshi Motoda	[67] [68]
58	Igor Mozetic	[1] [3] [4] [26] [91] [94] [113] [124]
59	Petra Kralj Novak (Petra Kralj)	[86] [95] [100] [102] [111] [112] [114] [124] [125]
60	Masayuki Numao	[35]
61	Gregor Papa	[79] [92]
62	Mor Peleg	[128]
63	Marko Petek	[124]
64	Ingrid Petric	[130]
65	Iztok A. Pilih	[28]
66	Vladimir Pirnat	[5] [11]
67	Joël Plisson	[91] [105]
68	Vid Podpecan	[118] [119] [121] [124] [129]
69	Tine S. Prevec	[28]
70	Aleksander Pur	[80] [82] [90] [97]
71	Luc De Raedt	[12] [13] [17] [20]
72	Simon Rawles	[62]
73	John Shawe-Taylor	[101]
74	Borut Sluban	[117] [120] [127]
75	Tomislav Smuc	[47]
76	Larisa N. Soldatova	[125]
77	Olga Stepánková	[22]
78	Cyril Masia Tissot	[87]
79	Ljupco Todorovski	[46] [58] [65] [66] [70] [73] [89]
80	Hannu Toivonen	[124]
81	Jakub Tolar	[71] [85] [88] [103] [104]
82	Igor Trajkovski	[85] [88] [96] [99] [103] [104] [108] [124] [125]
83	Peter D. Turney	[25] [32]
84	Tanja Urbancic	[60] [72] [82] [130]
85	Tanja Urbani	[92]
86	Anze Vavpetic	[125]
87	Paola Velardi	[107]
88	Vanja Vuga	[29] [30]
89	Geoffrey I. Webb	[111] [114]
90	Irene Weber	[22]
91	Stefan Wrobel	[18] [21] [62]
92	Monika Záková	[87] [119] [122]
93	Filip Zelezný	[56] [62] [71] [74] [83] [85] [87] [88] [104] [109] [110] [122]
94	Igor Zelic	[29] [30]
95	Monika Zemenova	[129]
96	Blaz Zupan	[33] [39] [77] [115]
97	Darko Zupanic	[8] [22] [26]

Last update Sun Jun 3 16:06:10 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page