Contents

• Computational scientific discovery and inductive databases.
  AM-2002. In Proc. ICDM International Workshop on Active Mining. Maebashi City, Japan, December 2002.
  
  
• Relational Data Mining.
  In Proc. Theory and Applications of Relational Structures as Knowledge Instruments (TARSKI). Prague, November 2002.
  
  
• Learning in rich representations: Inductive logic programming and computational scientific discovery.
  ICML-2002. In Proc. Nineteenth International Conference on Machine Learning.
  ILP-2002. In Proc. Twelfth International Conference on Inductive Logic Programming.
  Sydney, Australia, July 2002.
  
  
• Relational reinforcement learning for agents in worlds with objects.
  AAMAS-2. In Proc. AISB Symposium on Adaptive Agents and Multi-Agent Systems. London, UK, April 2002.
  
  
• Discovery support system for medicine and genetics (D. Hristovski, B. Peterlin, S. Dzeroski)
  In Proc. 2nd Congress of Genetic Society of Slovenia with International Participation, Bled, September 13 - 17, 2000.
  
  
• Environmental applications of machine learning.
  AIRIES-98. Workshop on Artificial Intelligence Research In Environmental Science. Victoria, BC, Canada, October 1998.
  
  
• Inductive logic programming applications: An overview.
  CompulogNet. Area Meeting on Computational Logic and Machine Learning. Manchester, UK, June 1998.
  
  
• New AI techniques applied to modelling (I. Bratko and S. Dzeroski).
  In Proc. Second International Conference Design to Manufacture in Modern Industry, pages 359-372, University of Maribor, 1995.
  
  
• Inductive logic programming for knowledge discovery in databases: an overview.
  AAAI'93 Workshop on Knowledge Discovery in Databases, Washington DC, July 1993.
  
  
• Background knowledge and declarative bias in inductive concept learning (N. Lavrac and S. Dzeroski).
  In Proc. Third International Workshop on Analogical and Inductive Inference, pages 51-71, Springer, Berlin, 1992.

• An introduction to relational data mining.
  Summer School on Relational Data Mining. University of Helsinki, Helsinki, 2002.
  
  
• Automated ecological modelling with machine learning.
  Classical and Automated Ecological Modelling. Biotechnical Faculty, University of Ljubljana, Slovenia, September 1999.
  
  
• An overview of inductive logic programming applications.
  Inductive Logic Programming Tutorial Day. Madison, Wisconsin, July 1998.
  
  
• An overview of inductive logic programming applications.
  Summer School on Inductive Logic Programming and Knowledge Discovery in Databases. Prague, Czech Republic, September 1997.
  
  
• Dynamical system identification with machine learning.
  Let's Face Chaos through Non-linear Dynamics, International Summer School, University of Ljubljana, Slovenia, September 1993.
  
  
• Environmental applications of machine learning.
  EAIA-98. International Summer School on KDD and Data Mining: Methods and Applications.Caminha, Portugal, September-October 1998.
  
  
• Inductive logic programming.
  MLKDD-98. I Brazilian School on Machine Learning and KDD. Rio de Janeiro, September-October 1998.
  
  
• Dynamical system identification with machine learning.
  Let's Face Chaos through Non-linear Dynamics, International Summer School, University of Ljubljana, Slovenia, September 1993.

• Relational reinforcement learning for agents in worlds with objects.
  Stanford University, Stanford, April 2003.
  
  
• Computational scientific discovery and inductive databases.
  Istituto di Sziencia e Tecnologie dell' Informazione (ISTI), Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy, March 2003.
  
  
• Is combining classifiers better than selecting the best one?.
  Osaka University, Osaka, Japan, December 2002.
  
  
• Relational data mining.
  NTT Communication Science Laboratories, Kyoto, Japan, December 2002.
  
  
• Is combining classifiers better than selecting the best one?.
  Department of Computer Science, Katholieke Universiteit, Leuven, June 2002.
  
  
• Discovery ordinary and partial differential equations.
  Oxford University, Oxford, UK, April 2002.
  
  
• Theory revision in equation discovery.
  Albert-Ludwigs Universität, Freiburg, February and March 2002.
  
  
• Is combining classifiers better than selecting the best one?.
  Austrian Research Institute for Artificial Intelligence, Vienna, February 2002.
  
  
• Equation discovery for dynamic systems identification.
  Stanford University, Stanford, September 2001.
  
  
• Environmental applications of machine learning.
  NASA Ames Research Center, Moffet Field, California. September, 2000.
  
  
• Equation Discovery.
  Katholieke Universiteit Leuven, Leuven, Belgium. December 1998.
  
  
• Applications of Inductive Logic Programming: An Overview.
  Imperial College, London, UK. October 1998.
  
  
• Relational Reinforcement Learning.
  University of Porto, Porto, Portugal. October 1998.
  
  
• Environmental applications of machine learning.
  Department of Computer Science, University of York, York, UK. April 1998.
  
  
• Slovene natural language resources and how to learn from them using ILP.
  XEROX Research Center Europe, Grenoble, France. January 1998. With T. Erjavec.
  
  
• Machine learning applications in biological monitoring of river water quality.
  School of Computing, Staffordshire University, Stafford, UK. November 1997.
  
  
• Slovene natural language resources and how to learn from them using ILP.
  Department of Computer Science, University of York, York, UK. November 1997.
  
  
• Knowledge discovery for diterpene structure elucidation from ¹³C NMR spectra.
  Department of Computer Science, University of Cyprus, Nicosia, Cyprus. October 1996.
  
  
• Diterpene structure elucidation from ¹³C NMR spectra with machine learning.
  Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH) Science and Technology Park of Crete, Heraklion, Greece. May 1996.
  
  
• Knowledge discovery for diterpene structure elucidation from ¹³C NMR spectra.
  Centrum voor Wiskunde en Informatica, Amsterdam, Netherlands. April 1996.
  
  
• Numerical constraints and learnability in inductive logic programming.
  Institute for Applied Information Technology, German National Research Center for Computer Science, Sankt Augustin, Germany. May 1995.
  
  
• Learning first-order clausal theories.
  Department of Computer Science, University of California at Santa Cruz, Santa Cruz, CA, USA. November 1994.
  
  
• Introduction to inductive logic programming.
  Robotics Laboratory, Stanford University, Palo Alto, CA, USA. November 1994.
  
  
• First order jk-clausal theories are PAC-learnable!
  Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium. December 1993.
  
  
• Dynamical system identification with machine learning.
  Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium. November 1993.
  
  
• Inductive logic programming for knowledge discovery in databases: an overview.
  AT&T Bell Laboratories, Murray Hill, NJ, USA. July 1993.
  
  
• PAC-learnability of restricted logic programs.
  Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan. June 1992.
  
  
• PAC-learnability of restricted logic programs.
  Artificial Intelligence Laboratory, University of New South Wales, Sydney, Australia. May 1992.
  
  
• Handling noise in inductive logic programming.
  The Turing Institute, Glasgow, Scotland. December 1991.
  
  
• The role of probability estimates in machine learning.
  The Turing Institute, Glasgow, Scotland. November 1991.
  
  
• Machine learning and inductive logic programming.
  Institute for Information Science, Faculty of Mathematics and Natural Sciences, University of Skopje, Macedonia. May 1991.
  
  
• Learning restricted Horn clauses from noisy examples.
  The Turing Institute, Glasgow, Scotland. January 1991.

• Multi-Relational Data Mining (S. Dzeroski and L. De Raedt).
  KDD-2003. Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, August 2003.
  
  
• Relational Data Mining.
  SDM-2003. SIAM International Conference on Data Mining. San Francisco, USA, May 2003.
  
  
• Relational Data Mining.
  IEEE International Conference on Data Mining. Maebashi City, Japan, December 2002.
  
  
• Inductive Logic Programming (N. Lavrac and S. Dzeroski).
  Fourth Scandinavian Conference on Artificial Intelligence, Stockholm, Sweeden, 1993.