ILPnet2 library: N. Lavrac

ILP publications by N. Lavrac

This is a list of all publications in the ILPnet2 on-line library which are (co-)authored by N. Lavrac. The title of the article is a link to the full reference, including -- if provided by the author -- abstract and URL. It also allows easy access to related publications.

2003

M.-A. Krogel, S. Rawles, F. Zelezný, P.A. Flach, N. Lavrac, and S. Wrobel. Comparative Evaluation of Approaches to Propositionalization. In T. Horváth and A. Yamamoto, editors, Proceedings of the 13th International Conference on Inductive Logic Programming, volume 2835 of Lecture Notes in Artificial Intelligence, pages 197--214. Springer-Verlag, 2003.

2002

D. Gamberger and N. Lavrac. Generating Actionable Knowledge by Expert-Guided Subgroup Discovery. In T. Elomaa, H. Mannila, and H. Toivonen, editors, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, number 2431 in Lecture Notes in Artificial Intelligence, pages 163--174. Springer-Verlag, August 2002.
N. Lavrac, F. Zelezný, and P. A. Flach. RSD: Relational subgroup discovery through first-order feature construction. In S. Matwin and C. Sammut, editors, Proceedings of the 12th International Conference on Inductive Logic Programming, volume 2583 of Lecture Notes in Artificial Intelligence, pages 149--165. Springer-Verlag, 2003.
Peter Flach and Nada Lavrac. Learning in Clausal Logic: A Perspective on Inductive Logic Programming. In Antonis C. Kakas and Fariba Sadri, editors, Computational Logic: Logic Programming and Beyond (Essays in Honour of Robert A. Kowalski), volume 2407 of Lecture Notes in Artificial Intelligence, pages 437--471. Springer-Verlag, Berlin, 2002.

2001

Nada Lavrac and Peter A. Flach. An extended transformation approach to inductive logic programming. ACM Transactions on Computational Logic, 2(4):458--494, October 2001. More behind this link.
Saso Dzeroski and Nada Lavrac. Introduction to Inductive Logic Programming. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 48--73. Springer-Verlag, September 2001. More behind this link.
Saso Dzeroski and Nada Lavrac, editors. Relational Data Mining. Springer-Verlag, Berlin, September 2001. More behind this link.
Stefan Kramer, Nada Lavrac, and Peter Flach. Propositionalization Approaches to Relational Data Mining. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 262--291. Springer-Verlag, September 2001. More behind this link.
Ljupco Todorovski, Irene Weber, Nada Lavrac, Olga Stepankova, Saso Dzeroski, Dimitar Kazakov, Darko Zupanic, and Peter Flach. Internet Resources on ILP for KDD. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 375--388. Springer-Verlag, September 2001. More behind this link.
D. Gamberger and N. Lavrac. Filtering noisy instances and outliers. In H. Liu and H. Motoda, editors, Instance Selection and Construction for Data Mining, pages 375--394. Kluwer Academic Publishers, Boston/Dordrecht/London, February 2001.

2000

Ljupco Todorovski, Peter Flach, and Nada Lavrac. Predictive Performance of Weighted Relative Accuracy. In Djamel A. Zighed, Jan Komorowski, and Jan Zytkow, editors, 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000), pages 255--264. Springer-Verlag, September 2000. More behind this link.
Peter A. Flach and Nada Lavrac. The role of feature construction in inductive rule learning. In Luc De Raedt and Stefan Kramer, editors, Proceedings of the ICML2000 workshop on Attribute-Value and Relational Learning: crossing the boundaries, pages 1--11, Stanford, USA, July 2000. More behind this link.
Nada Lavrac and Peter Flach. An extended transformation approach to Inductive Logic Programming. Technical Report CSTR-00-002, Department of Computer Science, University of Bristol, March 2000. More behind this link.
Ljupco Todorovski, Peter Flach, and Nada Lavrac. A report on experiments with weighted relative accuracy in CN2. Technical Report CSTR-00-003, Department of Computer Science, University of Bristol, March 2000. More behind this link.

1999

N. Lavrac, D. Gamberger, and V. Jovanoski. A study of relevance for learning in deductive databases. Journal of Logic Programming, 40(2/3):215--249, August/September 1999.
N. Lavrac, P. Flach, and B. Zupan. Rule Evaluation Measures: A Unifying View. In S. Dzeroski and P. Flach, editors, Proceedings of the 9th International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Artificial Intelligence, pages 174--185. Springer-Verlag, 1999.

1998

Nada Lavrac, Dragan Gamberger, and Peter Turney. A relevancy filter for constructive induction. IEEE Intelligent Systems, 13(2):50--56, March-April 1998.

1997

D. Zupanic, M. Hodoscek, N. Lavrac, and I. Mozetic. Global energy minimization of small molecules combining Constraint Logic Programming and molecular mechanics. Journal of Chemical Information and Computer Sciences, 37(6):966--971, December 1997. More behind this link.
P.A. Flach and N. Lavrac. Proceedings of the CompulogNet Area Meeting on Representation issues in reasoning and learning. Technical Report CSTR-97-005, Department of Computer Science, University of Bristol, 1997. More behind this link.

1996

D. Gamberger and N. Lavrac. Noise detection and elimination applied to noise handling in a KRK chess endgame. In S. Muggleton, editor, Proceedings of the 6th International Workshop on Inductive Logic Programming, volume 1314 of Lecture Notes in Artificial Intelligence, pages 72--88. Springer-Verlag, 1996.
N. Lavrac, S. Dzeroski, and I. Bratko. Handling Imperfect Data in Inductive Logic Programming. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 48--64. IOS Press, 1996.
L. De Raedt and N. Lavrac. Multiple Predicate Learning in two Inductive Logic Programming Settings. Journal on Pure and Applied Logic, 4(2):227--254, 1996.

1995

N. Lavrac, D. Gamberger, and S. Dzeroski. An approach to Dimensionality Reduction in Learning from Deductive Databases. In L. De Raedt, editor, Proceedings of the 5th International Workshop on Inductive Logic Programming, pages 337--354. Department of Computer Science, Katholieke Universiteit Leuven, 1995.
N. Lavrac and L. De Raedt. Inductive logic programming: A survey of European research. AI Communications, 8(1):3--19, 1995.

1994

N. Lavrac. Inductive logic programming. In N.E. Fuchs and G. Gottlob, editors, Proceedings of the 10th Logic Programming Workshop, Technical Report, IFI-94.10. Zürich University, 1994. (Invited paper)
N. Lavrac. Inductive concept learning using background knowledge. In T. Pequeno and F. Carvalho, editors, Proceedings of the 11th Brasilian Symposium on Artificial Intelligence, pages 1--16, 1994. (Invited paper)
N. Lavrac and S. Dzeroski. Weakening the language bias in LINUS. Journal of Experimental and Theoretical Artificial Intelligence, 6(1):95--119, 1994.
N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.

1993

S. Dzeroski and N. Lavrac. Inductive learning in deductive databases. IEEE Transactions on Knowledge and Data Engineering, 5(6):939--949, 1993.
N. Lavrac, S. Dzeroski, V. Pirnat, and V. Krizman. The utility of background knowledge in learning medical diagnostic rules. Applied Artificial Intelligence, 7:273--293, 1993.
L. De Raedt and N. Lavrac. The many faces of inductive logic programming. In J. Komorowski and Z.W. Raś, editors, Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems, volume 689 of Lecture Notes in Artificial Intelligence, pages 435--449. Springer-Verlag, 1993. (Invited paper)
L. De Raedt, N. Lavrac, and S. Dzeroski. Multiple Predicate Learning. In R. Bajcsy, editor, Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1037--1043. Morgan Kaufmann, 1993.
L. De Raedt, N. Lavrac, and S. Dzeroski. Multiple predicate learning. In S. Muggleton, editor, Proceedings of the 3rd International Workshop on Inductive Logic Programming, pages 221--240. J. Stefan Institute, 1993.

1992

S. Dzeroski and N. Lavrac. Refinement Graphs for FOIL and LINUS. In S. Muggleton, editor, Inductive Logic Programming, pages 319--334. Academic Press, 1992.
M. Kovacic, N. Lavrac, M. Grobelnik, D. Zupanic, and D. Mladenić. Stochastic search in inductive logic programming. In B. Neumann, editor, Proceedings of the 10th European Conference on Artificial Intelligence, pages 444--445. John Wiley, 1992.
N. Lavrac, B. Cestnik, and S. Dzeroski. Search heuristics in empirical inductive logic programming. In C. Rouveirol, editor, Proceedings of the ECAI-92 Workshop on Logical Approaches to Machine Learning, 1992.
N. Lavrac, B. Cestnik, and S. Dzeroski. Use of heuristics in empirical inductive logic programming. In S. Muggleton, editor, Proceedings of the 2nd International Workshop on Inductive Logic Programming, Report ICOT TM-1182, 1992.
N. Lavrac and S. Dzeroski. Inductive Learning of Relations from Noisy Examples. In S. Muggleton, editor, Inductive Logic Programming, pages 495--516. Academic Press, 1992.
N. Lavrac and S. Dzeroski. Background knowledge and declarative bias in inductive concept learning. In K. Jantke, editor, Proceedings 3rd International Workshop on Analogical and Inductive Inference, pages 51--71. Springer-Verlag, 1992. (Invited paper)

1991

S. Dzeroski and N. Lavrac. Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. In L. Birnbaum and G. Collins, editors, Proceedings of the 8th International Workshop on Machine Learning, pages 399--402. Morgan Kaufmann, 1991.
S. Dzeroski and N. Lavrac. Learning relations from imperfect data. Technical Report IJS-DP-6163, Jo\v zef Stefan Institute, 1991.
N. Lavrac and S. Dzeroski. Inductive Learning of Relational Descriptions from Noisy Examples. In S. Muggleton, editor, Proceedings of the 1st International Workshop on Inductive Logic Programming, pages 259--278, 1991.
N. Lavrac, S. Dzeroski, and M. Grobelnik. Learning Nonrecursive Definitions of Relations with LINUS. In Y. Kodratoff, editor, Proceedings of the 5th European Working Session on Learning, volume 482 of Lecture Notes in Artificial Intelligence, pages 265--281. Springer-Verlag, 1991.
N. Lavrac, S. Dzeroski, V. Pirnat, and [[V. Kri\v zman]]. Learning rules for early diagnosis of rheumatic diseases. In Proceedings of the 3rd Scandinavian Conference on Artificial Intelligence, pages 138--149. IOS Press, 1991.

1990

N. Lavrac. Principles of knowledge acquisition in expert systems. PhD thesis, Faculty of Technical Sciences, University of Maribor, 1990.

1988

I. Mozetic and N. Lavrac. Incremental learning from examples in a logic-based formalism. In P. Brazdil, editor, Proceedings of the Workshop on Machine Learning, Meta-Reasoning and Logics, pages 109--127, 1988.