Scientific publications of Jan Ramon (1998 - September 2008)

A1. Articles in international reviewed journals

  1. Geert Meyfroidt, Fabian Güiza, Dominiek Cottem, Wilfried De Becker, Kristien Van Loon, Jean-Marie Aerts, Daniël Berckmans, Jan Ramon, Maurice Bruynooghe and Greet Van den Berghe , Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model . BMC Medical Informatics and Decision Making, 11:64, 2011
  2. T. Calders, J. Ramon and D. Van Dyck, All normalized anti-monotonic overlap graph measures are bounded. Data Mining and Knowledge Discovery, 23(3) 503-548, 2011 (impact factor 2.421)
  3. A. Cano Odena, M. Spilliers, T. Dedroog, K. De Grave, J. Ramon and I.F.J. Vankelecom. Micropollutant removal via genetic algorithms and high throughput experimentation Journal of Membrane Science, 366(1-2), 25-32, 2010. (impact factor = 3.203)
  4. L. Schietgat, F. Costa, J. Ramon and L. De RaedtEffective Feature Construction by Maximum Common Subgraph Sampling, Machine Learning, 83(2)137-161, 2011 (impact factor = 1.663)
  5. T. Horvath and J. RamonEfficient frequent connected subgraph mining in graphs of bounded treewidth, Theoretical Computer Science, 411, 2784-2797. 2010. (impact factor = 0.943)
  6. T. Horvath, J. Ramon and S. WrobelFrequent subgraph mining in outerplanar graphs, Knowledge Discovery and Data Mining 21(3) 472-508, 2010 (impact factor = 2.421)
  7. D. Fierens, J. Ramon, H. Blockeel and M. Bruynooghe, A comparison of pruning criteria for probability trees, Machine learning, 78 (1-2), 251-285, 2010 (impact factor= 2.326)
  8. D. Fierens, J. Ramon, M. Bruynooghe and H. Blockeel, Learning Directed Probabilisitic Logical Models: Ordering-Search versus Structure-Search, Annals of Mathematics and Artificial Intelligence 54 (1), 99-133, 2008 (impact factor 0.588)
  9. K. Van Loon, F. Guiza, G. Meyfroidt, J-M. Aerts, J. Ramon, H. Blockeel, M. Bruynooghe, G. Van den Berghe, D. Berckmans, Prediction of clinical conditions after coronary bypass surgery using dynamic data analysis, Journal of Medical Systms 34(3) 229-239. 2010 (impact factor 0.45)
  10. L. De Raedt and J.Ramon, Deriving distance metrics from generality relations, Pattern Recognition Letters 30, pp. 187-191, 2009 (impact factor 0.853)
  11. J. Ramon and S. Nijssen, Polynomial-delay enumeration of monotonic graph classes, Journal of Machine Learning Research 10(Apr), pp. 907--929, 2009 (impact factor 2.682)
  12. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel, and M. Bruynooghe, Generalized ordering-search for learning directed probabilistic logical models, Machine Learning, 70:(2-3), pp. 169-188, 2008 (impactfactor = 3.258)
  13. N. Form, R. Burbidge, J. Ramon, and J. Whitaker, Parameterisation of an acousto-optic programmable dispersive filter for closed-loop learning experiments, Journal of Modern Optics, 55 (1), pp. 197-209, Januari, 2008. (impactfactor 1.189)
  14. J. Ramon, D. Fierens, F. Güiza, G. Meyfroidt, H. Blockeel, M. Bruynooghe, and G. Van den Berghe, Mining data from intensive care patients, Advanced Engineering Informatics 21 (3), pp. 243-256, July, 2007. (impactfactor = 1.295)
  15. K. Driessens, J. Ramon, and T. Gaertner, Graph kernels and Gaussian processes for relational reinforcement learning, Mach. Learn., 2005, 64(1-3), pp. :91-119. (Impactfactor = 3.050)
  16. J. Struyf, J. Ramon, M. Bruynooghe, S. Verbaeten, and H. Blockeel, Compact representation of knowledge bases in inductive logic programming, Mach. Learn. 57 (3), pp. 305-333, December, 2004. (Impactfactor = 3.050)
  17. J. Ramon, Clustering and instance based learning in first order logic, AI Com. 15 (4), pp. 217-218, 2003. (Impactfactor = 0.829)
  18. H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele, Improving the efficiency of Inductive Logic Programming through the use of query packs, Journal of Artificial Intelligence Research 16 , pp. 135-166, 2002. (Impactfactor = 1.615, 43 ISI cites)
  19. J. Ramon, and M. Bruynooghe, A polynomial time computable metric between point sets, Acta Inform. 37 (10), pp. 765-780, 2001. (Impactfactor = 0.745, Cited half-life > 14 years, 22 ISI cites)

Other journals

  1. J. Ramon, F. Costa and C. Costa Florencio, StReBio'09: Statistical Relational Learning and Mining in Bioinformatics, SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining 11(2) 88-89, 2009.

B1. Book chapters

  1. Ross D. King, Amanda Schierz, Amanda Clare, Jem Rowland, Andrew Sparkes, Siegfried Nijssen and Jan Ramon, Inductive Queries for a Drug Designing Robot Scientist, Inductive Databases and Constraint-Based Data Mining, Part 4, 425-451, Springer, 2010
  2. Geert Meyfroidt, Fabian Güiza, Jan Ramon and Maurice Bruynooghe: Machine learning techniques to examine large patient databases. Best Practice & Research Clinical Anaesthesiology: Information Technology in Anaesthesia & Intensive Care 23(1) pp. 127-143, 2009
  3. Marc Ponsen, Tom Croonenborghs, Karl Tuyls, Jan Ramon, Kurt Driessens, Jaap Van den Herik, Learning with whom to communicate using relational reinforcement learning, in Robert Babuska, Frans C.A. Groen (Eds.). Interactive Collaborative Information Systems. pp. 45-63, Springer, 2010,

B2. Edited books

  1. J. Ramon, C. Florencio Costa, F. Costa and J. Kok, StReBio'09, Proceedings of the KDD 2008 Workshop on Statistical Relational mining and Learning in Bioinformatics, Paris, France, June 28th, 2009.
  2. J. Ramon, C. Florencio Costa, F. Costa and J. Kok, StReBio'08, Proceedings of the ECML/PKDD 2008 Workshop on Statistical Relational Learning in Bioinformatics, Antwerp, Belgium, September 19th, 2008.
  3. H. Blockeel, J. Ramon, J. Shavlik and P. Tadepalli. ILP 2007, Proceedings of the 17th International Conference on Inductive Logic Programming, Corvallis, Oregon, USA, June 19-21th 2007, Lecture Notes in Computer Science Vol 4894, 2007.

C1. Articles in proceedings of international conferences

i) Articles in proceedings with professional publisher

These articles are reviewed, usually by three reviewers, and published by professional publishers such as Springer-Verlag (the Lecture Notes in Computer Science (LNCS) series has SCI impactfactor 0.513 in 2004) and Morgan Kaufmann or published in high-standard web repositories (e.g. ACM digital library). For recent publications, the acceptance rate of the conference is mentioned
  1. Guy Van den Broeck, Kurt Driessens and Jan Ramon, Monte-Carlo Tree Search in Poker using Expected Reward Distributions, Proceedings of the 1st Asian Conference on Machine Learning (ACML), Lecture Notes in Computer Science vol. 5828 pp. 367-381, 2009, (acceptance rate= 25.7%)
  2. Marc Ponsen, Tom Croonenborghs, Karl Tuyls, Jan Ramon and Kurt Driessens, Learning with whom to communicate using relational reinforcement learning, Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems- Vol. 2 pages:1221-1222, May 2009 (acceptance rate = 44.7%)
  3. Toon Calders, Jan Ramon and Dries Van Dyck, Anti-Monotonic Overlap-Graph Support Measures, Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 73-82, 2008, (acceptance rate = 9.7%)
  4. Tamas Horvath and Jan Ramon, Efficient frequent connected subgraph mining in graphs of bounded treewidth, Proceedings of Principles and Practice of Knowledge Discovery in Databases 2008, Lecture Notes in Computer Science, vol:5211, 520-535 (acceptance rate=20%)
  5. Laura Antanas, Kurt Driessens, Tom Croonenborghs and Jan Ramon, Using decision trees as the answer network in temporal-difference networks. Proceedings of the 18th European Conference on Artificial Intelligence. pp. 847-848, Patras, Greece, 21-25, July 2008, accepted (acceptance rate = 40%)
  6. Marc Ponsen, Jan Ramon, Tom Croonenborghs, Kurt Driessens and Karl Tuyls, Bayes-relational opponent modeling in no-limit poker, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008 (acceptance rate=26%)
  7. Kurt De Grave, Jan Ramon, Luc De Raedt, Active Learning for High Throughput Screening, Proceedings of the 11th International Conference on Discovery Science (DS 2008), Lecture Notes in Computer Science, vol. 5255 pp:185-196, 2008 (acceptance rate = 44.8%)
  8. Leander Schietgat, Jan Ramon, Maurice Bruynooghe and Hendrik Blockeel, An efficiently computable graph-based metric for the classification of small molecules, Proceedings of the 11th International Conference on Discovery Science (DS 2008), Lecture Notes in Computer Science, Vol. 5255, pp. 197-209 (acceptance rate = 44.8%)
  9. J. Ramon, K. Driessens, and T. Croonenborghs, Transfer learning in reinforcement learning problems through partial policy recycling, Machine Learning: ECML 2007, 18th European Conference on Machine Learning, Proceedings (Kok, J. N. and Koronacki, J. and Lopez de Mantaras, R. and Matwin, S. and Mladenic, D. and Skowron, A., eds.), vol 4701, Lecture Notes in Computer Science, pp. 699-707, 2007 (acceptance rate = 23.8%)
  10. Daan Fierens, Jan Ramon, Maurice Bruynooghe and Hendrik Blockeel, Learning Directed Probabilistic Logical Models Using Ordering-Search, in Proceedings of the 17th International Conference on Inductive Logic Programming, Corvallis, USA, June 2007, Lecture notes in computer science vol. 4894 pp. 24-24
  11. C. Vens, J. Ramon, and H. Blockeel, ReMauve, a relational model tree learner, Inductive Logic Programming, ILP 2006, Revised Selected Papers (Muggleton, S. and Otero, R. and Tamaddoni-Nezhad, A., eds.), vol 4455, Lecture Notes in Computer Science, pp. 424-438, 2007 (acceptance rate = 44%)
  12. D. Fierens, J. Ramon, M. Bruynooghe, and H. Blockeel, Learning directed probabilistic logical models: Ordering-search versus structure-search, 18th European Conference on Machine Learning, Proceedings (Kok, J. N. and Koronacki, J. and Lopez de Mantaras, R. and Matwin, S. and Mladenic, D. and Skowron, A., eds.), vol 4701, Lecture Notes in Computer Science, pp. 567-574, 2007 (acceptance rate = 23.8%)
  13. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe, Online learning and exploiting relational models in reinforcement learning, IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence (Veloso, M., ed.), pp. 726-731, 2007 (acceptance rate = 34%)
  14. C. Vens, J. Ramon, and H. Blockeel, Refining aggregate conditions in relational learning, Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (Fürnkranz, J. and Scheffer, T. and Spiliopoulou, M., eds.), vol 4213, Lecture Notes in Artificial Intelligence, pp. 383-394, 2006 (acceptance rate = 22%)
  15. T. Horváth, J. Ramon, and S. Wrobel, Frequent subgraph mining in outerplanar graphs, Proceedings of The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA (Eliassi-Rad, T., ed.), pp. 191-198, 2006 (acceptance rate = 11%)
  16. T. Croonenborghs, K. Tuyls, J. Ramon, and M. Bruynooghe, Multi-agent relational reinforcement learning. Explorations in multi-state coordination tasks, Learning and Adaptation in Multi Agent Systems: First International Workshop , LAMAS 2005, Revised Selected Papers (Tuyls, K. and Verbeeck, K. and 't Hoen P. and Sen S., eds.), vol 3898, Lecture Notes in Computer Science, pp. 192-206, 2006
  17. J. Ramon, and T. Croonenborghs, Searching for compound goals using relevancy zones in the game of Go, Computers and Games: 4th International Conference, CG 2004, Revised Papers (van den Herik, J. and Bjornsson, Y. and Netanyahu, N., eds.), vol 3846, Lecture Notes in Computer Science, pp. 113-128, 2006 (acceptance rate = 51%)
  18. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, A comparison of approaches for learning probability trees, Machine Learning: ECML 2005, 16th European Conference on Machine Learning, Proceedings (Joao, G. and Camacho, R. and Pavel, B. and Alipio, J. and Luis, T., eds.), vol 3720, Lecture Notes in Computer Science, pp. 556-563, 2005 (acceptance rate = 25%)
  19. D. Fierens, H. Blockeel, M. Bruynooghe, and J. Ramon, Logical Bayesian networks and their relation to other probabilistic logical models, Proceedings of the 15th International Conference on Inductive Logic Programming (Kramer, S. and Pfharinger, B, eds.), vol 3625, Lecture Notes in Computer Science, pp. 121-135, 2005 (acceptance rate = 51%)
  20. L. De Raedt, and J. Ramon, Condensed representations for Inductive Logic Programming, Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004) (Dubois, D. and Welty C., eds.), pp. 438-446, 2004 (acceptance rate = 31%)
  21. T. Gartner, K. Driessens, and J. Ramon, Graph kernels and Gaussian processes for relational reinforcement learning, Inductive Logic Programming, 13th International Conference, ILP 2003, Proceedings (Horvath, T. and Yamamoto, A., eds.), vol 2835, Lecture Notes in Computer Science, pp. 146-163, 2003 (acceptance rate = 40%)
  22. K. Driessens, and J. Ramon, Relational instance based regression for relational reinforcement learning, Proceedings of the Twentieth International Conference on Machine Learning (Fawcett, T. and Mishra, N., eds.), pp. 123-130, 2003
  23. J. Ramon, T. Francis, and H. Blockeel, Learning a Tsume-Go heuristic with Tilde, Computers and Games, CG2000, Revised Papers (Marsland, T. A. and Frank, I., eds.), vol 2063, Lecture Notes in Computer Science, pp. 151-169, 2001
  24. J. Struyf, J. Ramon, and H. Blockeel, Compact representation of knowledge bases in ILP, Inductive Logic Programming, 12th International Conference, ILP 2002, Revised Papers (Matwin, S. and Sammut, C., eds.), vol 2583, Lecture Notes in Computer Science, pp. 254-269, 2003
  25. K. Driessens, J. Ramon, and H. Blockeel, Speeding up relational reinforcement learning through the use of an incremental first order decision tree algorithm, Proceedings of ECML - European Conference on Machine Learning (De Raedt, Luc and Flach, Peter, eds.), vol 2167, LNAI, pp. 97-108, 2001
  26. H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele, Executing query packs in ILP, Inductive Logic Programming, 10th International Conference, ILP2000, London, UK, July 2000, Proceedings (James Cussens and Alan Frisch, eds.), vol 1866, Lecture Notes in Artificial Intelligence, pp. 60-77, 2000
  27. J. Ramon, and L. De Raedt, Instance based function learning, Proceedings of the Ninth International Workshop on Inductive Logic Programming (Dzeroski, S. and Flach, P., eds.), vol 1634, LNAI, pp. 268-278, 1999
  28. S. Nienhuys-Cheng, W. Van Laer, J. Ramon, and L. De Raedt, Generalizing refinement operators to learn prenex conjunctive normal forms, Proceedings of the Ninth International Workshop on Inductive Logic Programming (Dzeroski, S. and Flach, P., eds.), vol 1634, LNAI, pp. 245-256, 1999
  29. J. Ramon, and M. Bruynooghe, A framework for defining distances between first-order logic objects, Proceedings of 8th International Conference on Inductive Logic Programming (ILP'98), Madison, Wisconsin, USA (Page, D., ed.), vol 1446, Lecture Notes in Computer Science, pp. 271-280, 1998
  30. H. Blockeel, L. De Raedt, and J. Ramon, Top-down induction of clustering trees, Proceedings of the 15th International Conference on Machine Learning (Shavlik, J., ed.), pp. 55-63, 1998

ii) Articles in other proceedings

Reviewed articles (at least two reviewers) are marked with (*)
  1. Christophe Costa Florencio, Jan Ramon, Jonny Daemen, Jan Van den Bussche, Dries Van Dyck, Graph grammars as language bias in graph mining, Proceedings of the 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium, 2009 (*)
  2. Leander Schietgat, Fabrizio Costa, Jan Ramon and Luc De Raedt, Maximum common subgraph mining: A fast and effective approach towards feature generation, Proceedings of the 7th International Workshop on Mining and Learning with Graphs, Leuven, Belgium, 2009 (*)
  3. Kurt De Grave, Jan Ramon and Luc De Raedt, Active learning for primary drug screening, Proceedings of the Annual Belgian-Dutch Machine Learning Conference 2008, pp. 55-56, 2008
  4. Toon Calders, Jan Ramon and Dries Van Dyck, Min, max and PTime anti-monotonic overlap graph measures, Proceedings of the 6th International Workshop on Mining and Learning with Graphs, pp. 1-3, July 2008 (*)
  5. Marc Ponsen, Jan Ramon, Tom Croonenborghs, Kurt Driessens and Karl Tuyls, Bayes-relational opponent modeling in poker, Proceedings of the Annual Belgian-Dutch Conference on Machine Learning (Louis Wehenkel, Pierre Geurts and Raphael Maree eds.), May 2008
  6. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Benelearn 2007, Annual Machine Learning Conference of Belgium and the Netherlands (van Someren, M. and Katrenko, S. and Adriaans, P., eds.), pp. 97-104, 2007
  7. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Proceedings of the 5th International Workshop on Mining and Learning with Graphs (Frasconi, P. and Kersting, K. and Tsuda, K., eds.), pp. 67-70, 2007
  8. J. Ramon, S. Dubrovskaya, and H. Blockeel, Learning resistance mutation pathways of HIV, Proceedings of The Sixteenth Annual Machine Learning Conference of Belgium and the Netherlands, Amsterdam, The Netherlands, 2007.
  9. J. Ramon, and S. Nijssen, General graph refinement with polynomial delay, Proceedings of Mining and Learning with Graphs 2007, Florence, Italy, 2007
  10. R. Goetschalckx, and J. Ramon, Using expert knowledge to construct error bound state-action aggregations for reinforcement learning, Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence (Dastani, M. and de Jong, E., eds.), pp. 127-134, 2007
  11. F. Güiza, D. Fierens, J. Ramon, H. Blockeel, G. Meyfroidt, M. Bruynooghe, and G. Van den Berghe, Predictive data mining in intensive care, Annual Machine Learning Conference of Belgium and the Netherlands, Benelearn 2006 (Saeys, Y. and Tsiporkova, R. and De Baets, B. and Van de Peer, Y., eds.), pp. 81-88, 2006
  12. T. Horváth, J. Ramon, and S. Wrobel, Frequent subgraph mining in outerplanar graphs, Proceedings of the International Workshop on Mining and Learning with Graphs (MLG-2006) (Gaertner, T. and Carriga, G.C. and Meinl, T., eds.), pp. 37-48, 2006
  13. T. Horváth, J. Ramon, and S. Wrobel, Frequent subgraph mining in outerplanar graphs, Proceedings of the Eighteenth Belgian-Dutch Conference on Artificial Intelligence, 2006
  14. A. Van Assche, J. Ramon, and H. Blockeel, Learning an interpretable model from an ensemble in ILP, ILP'06, 16th International Conference on Inductive Logic Programming, Short Papers (Muggleton, S. and Otero, R., eds.), pp. 210-212, 2006
  15. J. Ramon, Efficient mining of frequent outerplanar graphs, ILP'06, 16th International Conference on Inductive Logic Programming, Short Papers (Muggleton S., and Otero, R., eds.), pp. 170-172, 2006
  16. J. Ramon, Predicting evolution of critically ill patients, Proceedings of the KDD 2006 workshop on Theory and Practice of Temporal Data Mining (Li, T., ed.), pp. 1-3, 2006
  17. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel, and M. Bruynooghe, Generalizing ordering-search for learning directed probabilistic logical models, ILP'06, 16th International Conference on Inductive Logic Programming, Short Papers (Muggleton, S. and Otero, R., eds.), pp. 173-175, 2006
  18. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe. Model-Assisted Approaches for Relational Reinforcement Learning: Some Challenges for the SRL Community. In Proceedings of the ICML-2006 Workshop on Open Problems in Statistical Relational Learning, Pittsburgh, PA, 2006.
  19. C. Vens, J. Ramon and H. Blockeel, ReMauve: a relational model-tree learner. Proceedings of the 16th International Conference on Inductive Logic Programming, short papers, pp. 216-218, 2006
  20. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel, and M. Bruynooghe, Generalized ordering-search for learning directed probabilistic logical models, Inductive Logic Programming, ILP 2006, Revised Selected Papers (Muggleton, S. and Otero, R. and Tamaddoni-Nezhad, A., eds.), vol 4455, Lecture Notes in Computer Science, pp. 40-42, 2007
  21. K. Driessens, J. Ramon, and T. Croonenborghs. Transfer learning for reinforcement learning through goal and policy parametrization. In proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning (Online Proceedings: http://www.cs.utexas.edu/%7Ebanerjee/icmlws06/), 2006.
  22. F. Güiza, D. Fierens, J. Ramon, H. Blockeel, G. Meyfroid, M. Bruynooghe, and G. Van Den Berghe, Predictive data mining in intensive care, Annual Machine Learning Conference of Belgium and the Netherlands, Benelearn 2006 (Saeys, Y. and Tsiporkova, R. and De Baets, B. and Van de Peer, Y., eds.), pp. 81-88, 2006
  23. F. Güiza, J. Ramon, and H. Blockeel, Gaussian processes for prediction in intensive care, Proceedings of Gaussian Processes in Practice Workshop, Bletchley Park, U.K. (Lawrence, N.D. and Schwaighofer, A. and Quinonero, J., eds.), 2006
  24. K. Driessens, J. Ramon, and T. Croonenborghs, Transfer learning for reinforcement learning through goal and policy parametrization, Proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning (Online Proceedings) (Banerjee, B. and Liu, Y. and Youngblood, G.M., eds.), pp. 1-4, 2006
  25. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe, Model-assisted approaches for relational reinforcement learning: some challenges for the srl community, Proceedings of the ICML-2006 Workshop on Open Problems in Statistical Relational Learning (Fern, A. and Getoor, L. and Milch, B., eds.), pp. 1-8, 2006
  26. J. Ramon, Predicting evolution of critically ill patients. In Li Toa, Charles Perng, Haixun Wang, and Carlotta Domeniconi, editors, Proceedings of the KDD-workshop on Theory and Practice of Temporal Data Mining}, pages 1--3, Philadelphia, PA, USA, 2006.
  27. J. Ramon, T. Horvath, L. Schietgat and S. Wrobel, FOG: Finding Outerplanar Graphs, Demo session at KDD'2006 (Gabor Melli ed.), pp. 1-3, 2006 (acceptance rate = 40%)
  28. K. Tuyls, T. Croonenborghs, J. Ramon, R. Goetschalckx, and M. Bruynooghe, Multi-agent relational reinforcement learning, Proceedings of the First International Workshop on Learning and Adaptation in Multi Agent Systems (Tuyls, K. and Verbeeck, K. and 't Hoen, P. and Sen, S., eds.), pp. 123-132, 2005 (*)
  29. D. Fierens, H. Blockeel, M. Bruynooghe, and J. Ramon, Logical Bayesian networks and their relation to other probabilistic logical models, 17th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2005, Brussels, Belgium, October 17-18, 2005
  30. J. Ramon, On the convergence of reinforcement learning using a decision tree learner, Proceedings of ICML-2005 workshop on Rich Representation for Reinforcement Learning, Bonn, Germany (Driessens, K. and Fern, A., van Otterlo, M., eds.), 2005
  31. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, A comparison of approaches for learning first-order logical probability estimation trees, 15th International Conference on Inductive Logic Programming, Late-breaking papers (Kramer, S. and Pfharinger, B., eds.), pp. 11-16, 2005
  32. J. Ramon, and J. Struyf, Efficient theta-subsumption of sets of patterns, Benelearn 2004 - Annual Machine Learning Conference of Belgium and the Netherlands (Nowé, A. and Lenaerts, T. and Steenhaut, K., eds.), pp. 95-102, 2004
  33. J. Ramon, and K. Driessens, On the numeric stability of Gaussian processes regression for relational reinforcement learning, ICML-2004 Workshop on Relational Reinforcement Learning (Tadepalli, P. and Givan, R. and Driessens, K., eds.), pp. 10-14, 2004
  34. D. Fierens, H. Blockeel, J. Ramon, and M. Bruynooghe, Logical Bayesian networks, Proceedings of the 3nd international workshop on multi-relational data mining (Dzeroski, S. and Blockeel, H., eds.), pp. 19-30, 2004 (*)
  35. L. De Raedt, and J. Ramon, Condensed representations for inductive logic programming, Proceedings of the 14th International Conference on Inductive Logic Programming, Work in Progress Track (Camacho, R. and King, R. and Srinivasan, A., eds.), pp. 25-34, 2004
  36. T. Croonenborghs, J. Ramon, and M. Bruynooghe, Towards informed reinforcement learning, Proceedings of the ICML'04 workshop on relational reinforcement learning (Tadepalli, P. and Givan, R. and Driessens, K., eds.), pp. 21-26, 2004
  37. J. Ramon, and T. Gaertner, Expressivity versus efficiency of graph kernels, Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences (Washio, T. and De Raedt, L., eds.), pp. 65-74, 2003 (*)
  38. J. Ramon, and J. Struyf, Computer science in issues in Baduk, Proceedings of the 2nd International Conference on Baduk (Chihyung, N., ed.), pp. 163-182, 2003
  39. J. Ramon, N. Jacobs, and H. Blockeel, Opponent modeling by analysing play, Proceedings of the First Workshop on Agents in Computer Games at the Third International Conference on Computers and Games (Bowling, M. and Kaminka, G. and Vincent, R., eds.), pp. 1-8, 2002 (*)
  40. H. Blockeel, M. Bruynooghe, S. Dzeroski, J. Ramon, and J. Struyf, Hierarchical multi-classification, KDD-2002 Workshop Notes: MRDM 2002, Workshop on Multi-Relational Data Mining (De Raedt, L. and Dzeroski, S. and Wrobel, S., eds.), pp. 21-35, 2002 (*)
  41. J. Ramon, and H. Blockeel, A survey of the application of machine learning to the game of go, Proceedings of the First International Conference on Baduk (Sang-Dae Hahn, ed.), pp. 1-10, 2001
  42. H. Blockeel, K. Driessens, N. Jacobs, R. Kosala, S. Raeymaekers, J. Ramon, J. Struyf, W. Van Laer, and S. Verbaeten, First order models for the predictive toxicology challenge, ECML/PKDD Workshop : The Predictive Toxicology Challenge 2000-2001 (Helma, C. and King, R. and Kramer, S. and Srinivasan, A., eds.), pp. 1-12, 2001 (*)
  43. J. Ramon, and L. Dehaspe, Using belief networks to neutralize known dependencies in conceptual clustering, 10th International Conference on Inductive Logic Programming, Work-in-Progress Reports (James Cussens and Alan Frisch, eds.), pp. 226-243, 2000
  44. J. Ramon, and L. Dehaspe, Using belief networks to neutralize known dependencies in conceptual clustering, The Fifth International Workshop on Multistrategy Learning (Ryzard Michalski and Pavel B. Brazdil, eds.), pp. 165-180, 2000 (*)
  45. J. Ramon, T. Francis, and H. Blockeel, Learning a Go heuristic with TILDE, Proceedings of the 12th Belgian-Dutch Artificial Intelligence Conference (Antal van den Bosch and Hans Weigand, eds.), pp. 149-156, 2000
  46. J. Ramon, and L. De Raedt, Multi instance neural networks, Proceedings of the ICML-2000 workshop on attribute-value and relational learning (Luc De Raedt and Stefan Kramer, eds.), pp. 53-60, 2000
  47. J. Ramon, and L. Dehaspe, Upgrading Bayesian Clustering to First Order Logic, Proceedings of the 9th Belgian-Dutch Conference on Machine Learning (Blockeel, Hendrik and Dehaspe, Luc, eds.), pp. 77-84, 1999
  48. H. Blockeel, L. Dehaspe, K. Driessens, N. Jacobs, R. Kosala, J. Ramon, and W. Van Laer, The Leuven submission to the Benelearn-99 competition, The Benelearn 1999 Competition (van der Putten, P. and van Someren, M., eds.), pp. 1-8, 1999
  49. J. Ramon, M. Bruynooghe, and D. De Schreye, Recent research results in the group declarative languages and artificial intelligence, Proceedings of the Benelux Workshop on Logic Programming (van Raamsdonk, Femke, ed.), pp. 1-11, 1998
  50. J. Ramon, M. Bruynooghe, and W. Van Laer, Distance measures between atoms, Proceedings of the CompulogNet Area Meeting on Computational Logic and Machine Learing (Lloyd, L., ed.), pp. 35-41, 1998 (*)

iii)Courses, with text

  1. J. Ramon, Association analysis, The HIV Data Management and Data Mining Workshop, South African Medical Research Council, 491 Ridge Road, Durban, South-Africa, December 16th, 2004, Molecular Virology and Bioinformatics Unit at Africa Centre for Health and Population Studies, 4h

C2) Contributions at international conferences, not published or only as abstract, and technical reports

i) Abstracts of contributions at conferences

  1. Tamas Horvath and Jan Ramon, Efficient frequent connected subgraph mining in graphs of bounded tree-width, Proceedings of the 2010 workshop on knowledge discovery, data mining and machine learning (KDML'10), Kassel, Germany, October 2010
  2. 1.Jan Ramon, Efficient search in molecular graph space to recognize mass spectra, Benelux Bioinformatics Conference 2009, Liege, Belgium, 2009
  3. Kurt De Grave, Jan Ramon and Luc De Raedt, Active learning for primary drug screens, Proceedings of the Spring Workshop on Mining and Learning 2008, Traben-trarbach, Germany, April 2008
  4. Angels Cano Odena, Pieter Vandezande, Isabelle Cools, Kris Vanderschoot, Kurt De Grave, Jan Ramon, Luc De Raedt and Ivo Vankelecom, Comparison of multi-parameter optimization strategies for the development of nanofiltration membranes for salt and micropollutants removal, Proceedings of the International Congress on Membranes and Membrane Processes 2008, Honolulu, Hawaii, 12-18 July 2008
  5. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Former Freiburg, Leuven and Friends Workshop on Machine Learning, FLF-07, Massembre (Heer), Belgium, March 21-23, 2007
  6. L. Schietgat, J. Ramon, J. Gagelmans, and H. Blockeel, Structure-based search for resistance development in HIV, Benelux Bioinformatics Conference, BBC 2007, Leuven, Belgium, November 12-13, 2007
  7. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Benelux Bioinformatics Conference, BBC 2007, Leuven, Belgium, November 12-13, 2007
  8. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel, and M. Bruynooghe, Generalized ordering-search for learning directed probabilistic logical models, The 31st Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications, GfKl2007, Freiburg, Germany, March 7-9, 2007
  9. Geert Meyfroidt, Fabian Güiza, Daan Fierens, Jan Ramon, Greet Van den Berghe: Data mining for the prediction of intensive care unit (ICU) length of stay (LOS). Intensive Care Medicine, September 2006, S13, pS57, n°209. (European Society of Intensive Care Medicine Meeting, September 25th, 2006, Barcelona, Spain)
  10. Fabian Guiza Grandas, Jan Ramon, Geert Meyfroidt, Hendrik Blockeel, Maurice Bruynooghe, Greet Van den Berghe, Machine learning methods for prediction in intensive care. Journal of Critical Care 21(4), pp. 353-354, 2006 (5th Conference on Complexity in Acute Illness, Washington DC, USA
  11. Fabian Guiza, Jan Ramon, Geert Meyfroidt, Hendrik Blockeel, Maurice Bruynooghe, Greet Van den Berghe, Predicting blood temperature using Gaussian processes, Journal of Critical Care 21(4), pp. 354-355, 2006 (5th Conference on Complexity in Acute Illness, Washington DC, USA
  12. J. Ramon, and S. Nijssen, Enumerating graphs, Former Freiburg, Leuven and Friends Workshop on Machine Learning, FLF-07, Massembre (Heer), Belgium, March 21-23, 2007
  13. R. Goetschalckx, and J. Ramon, On policy learning in restricted policy spaces, AAAI 2007 Student Abstract and Poster Program, Vancouver, Canada, July 22-26, 2007,
  14. S. Dubrovskaya, J. Ramon, and H. Blockeel, Learning resistance mutation pathways of HIV, Former Freiburg, Leuven and Friends Workshop on Machine Learning, FLF-07, Massembre (Heer), Belgium, March 21-23, 2007
  15. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe, Model-Assisted approaches for relational reinforcement learning, Former Freiburg, Leuven and Friends Workshop on Machine Learning, FLF-07, Massembre (Heer), Belgium, March 21-23, 2007
  16. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe, Model-assisted approaches for relational reinforcement learning, The 31st Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications, GfKl2007, Freiburg, Germany, March 7-9, 2007
  17. C. Vens, J. Ramon, and H. Blockeel, Refining aggregate conditions in relational learning, 18th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2006, Namur, Belgium, October 5-6, 2006
  18. J. Ramon, and T. Horváth, Efficient graph classes for frequent pattern mining, 7th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-06, Titisee, Germany, March 13-14, 2006
  19. T. Horváth, and J. Ramon, Mining d-tenuous outerplanar graphs, Joint APrIL/IQ Workshop, Titisee, Germany, March 15-18, 2006
  20. T. Horváth, J. Ramon, and S. Wrobel, Frequent subgraph mining in outerplanar graphs, 18th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2006, Namur, Belgium, October 5-6, 2006
  21. F. Güiza, D. Fierens, J. Ramon, H. Blockeel, G. Meyfroidt, M. Bruynooghe, and G. Van den Berghe, Predictive data mining in intensive care, Benelearn 2006, Gent, Belgium, May 11-12, 2006
  22. F. Güiza, J. Ramon, and H. Blockeel, Predictive data mining in intensive care, 7th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-06, Titisee, Germany, March 13-14, 2006
  23. R. Goetschalckx, J. Ramon, H. Blockeel, and M. Bruynooghe, Using expert knowledge to construct state-action aggregations for reinforcement learning, ICIS Third All Project Members Meeting, ICIS APM 3, Delft, The Netherlands, May 24, 2006
  24. R. Goetschalckx, and J. Ramon, Reinforcement learning with state-action-pair generalized aggregation, 7th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-06, Titisee, Germany, March 13-14, 2006
  25. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, A (further) comparison of approaches for learning probability trees, Joint APrIL/IQ Workshop, Titisee, Germany, March 15-18, 2006
  26. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, Randomisation tests for probability trees, 7th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-06, Titisee, Germany, March 13-14, 2006
  27. D. Fierens, H. Blockeel, J. Ramon, and M. Bruynooghe, A comparison of pruning criteria for probability trees, Annual Machine Learning Conference of Belgium and The Netherlands, Benelearn 2006, Gent, Belgium, May 11-12, 2006
  28. S. Dubrovskaya, J. Ramon, L. Schietgat, and H. Blockeel, Mining mutation pathways of HIV considering phylogenetic information, 12th Workshop on Virus Evolution and Molecular Epidemiology, Athens, Greece, September 10-15, 2006
  29. T. Croonenborghs, J. Ramon, H. Blockeel, and M. Bruynooghe, Learning a dynamic Bayesian network to do lookahead in Q-learning, 7th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-06, Titisee, Germany, March 13-14, 2006
  30. J. Ramon, On the convergence of relational reinforcement learning using a decision tree learner, Freiburg, Leuven and Friends Workshop, FLF'05, Ferrières, Belgium, March 7-9, 2005,
  31. D. Fierens, H. Blockeel, M. Bruynooghe, and J. Ramon, Logical Bayesian networks and their relation to other probabilistic logical models, 17th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2005, Brussels, Belgium, October 17-18, 2005
  32. J. Ramon, Active learning: The domain expert is not an oracle, 5th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-04, Hinterzarten, Germany, March 8-10, 2004
  33. J. Ramon, and J. Struyf, Frequent pattern mining under generalized subsumption, Dutch Belgian Database Day 2004, DBDBD 2004, Antwerpen, Belgium, December 3, 2004
  34. J. Ramon, and J. Struyf, On efficient mining of compactly represented sets of frequent patterns in relational languages, Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004,
  35. D. Fierens, H. Blockeel, and J. Ramon, Domain and combining rules in Bayesian logic programs, 5th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-04, Hinterzarten, Germany, March 8-10, 2004
  36. T. Croonenborghs, and J. Ramon, Informed reinforcement learning, 5th "Freiburg, Leuven and Friends" Workshop on Machine Leanring, FLF-04, Hinterzarten, Germany, March 8-10, 2004
  37. J. Struyf, J. Ramon, M. Bruynooghe, S. Verbaeten, and H. Blockeel, Compact representation of knowledge bases in inductive logic programming, 4th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-03, Leuven/Dourbes, Belgium, March 19-21, 2003,
  38. J. Ramon, Learning problems in the game of go, 4th "Freiburg, Leuven and Friends" Workshop on Machine Learning, FLF-03, Leuven/Dourbes, Belgium, March 19-21, 2003,
  39. K. Driessens, and J. Ramon, Relational instance based regression for relational reinforcement learning, 15th Belgian-Dutch Conference on Artificial Intelligence, BNAIC'03, Nijmegen, The Netherlands, October 23-24, 2003
  40. J. Struyf, J. Ramon, and H. Blockeel, Compact representation of knowledge bases in ILP, Belgian-Dutch Conference on Artificial Intelligence, BNAIC'02, Leuven, Belgium, October 21-22, 2002
  41. J. Struyf, J. Ramon, and H. Blockeel, Compact representation of knowledge bases in ILP, The Twelfth International Conference on Inductive Logic Programming, ILP 2002, Sydney, Australia, July 9-11, 2002,
  42. J. Ramon, and H. Blockeel, A prototype for the edit distance, 2nd Leuven-Freiburg Workshop on Machine Learning, LF-01, Oostkamp, Belgium, March 14-16, 2001
  43. K. Driessens, J. Ramon, and H. Blockeel, Speeding up relational reinforcement learning through the use of an incremental first order decision tree learner, Belgian-Dutch Artificial Intelligence Conference, BNAIC, Amsterdam, The Netherlands, October 25-26, 2001
  44. J. Ramon, Searching clusters in ILP, overview and further work, 1st Freiburg-Leuven Workshop on Machine Learning, Freiburg, Germany, April 25-26, 2000
  45. J. Ramon, and M. Bruynooghe, A framework for defining distance between first-order logic objects, Netherlands/Belgium Conference on Artificial Intelligence, NAIC'98, Amsterdam, The Netherlands, 18-19 November 1998

ii) Technical reports

  1. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, A comparison of pruning criteria for probability trees, K.U.Leuven, Department of Computer Science, Report CW 488, April, 2007
  2. D. Fierens, J. Ramon, M. Bruynooghe, and H. Blockeel, Learning directed probabilistic logical models: ordering-search versus structure-search, Department of Computer Science, K.U.Leuven, Report CW 490, Leuven, Belgium, May, 2007
  3. R. Goetschalckx, J. Ramon, H. Blockeel, and M. Bruynooghe, Using Expert Knowledge to Construct State-Action Aggregations for Reinforcement Learning, K.U.Leuven, Department of Computer Science, Report CW 445, May, 2006
  4. D. Fierens, J. Ramon, H. Blockeel, and M. Bruynooghe, A comparison of approaches for learning probability trees, Department of Computer Science, K.U.Leuven, Report CW 418, Leuven, Belgium, July, 2005
  5. J. Struyf, J. Ramon, M. Bruynooghe, S. Verbaeten, and H. Blockeel, Compact representation of knowledge bases in inductive logic programming, K.U.Leuven, Department of Computer Science, Report CW 377, May, 2004
  6. J. Ramon, and M. Bruynooghe, A polynomial time computable metric between point sets, Department of Computer Science, K.U.Leuven, Report CW 301, Leuven, Belgium, October, 2000
  7. H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele, Executing query packs in ILP, Department of Computer Science, K.U.Leuven, Report CW 287, Leuven, Belgium, May, 2000
  8. J. Ramon, M. Bruynooghe, and W. Van Laer, Distance measures between atoms, Department of Computer Science, K.U.Leuven, Report CW 264, Leuven, Belgium, June, 1998
  9. J. Ramon, and M. Bruynooghe, A framework for defining distances between first-order logic objects, Department of Computer Science, K.U.Leuven, Report CW 263, Leuven, Belgium, June, 1998

Thesis

  1. J. Ramon, Clustering and instance based learning in first order logic, Ph.D. Thesis, Department of Computer Science, K.U.Leuven, Leuven, Belgium, October, 2002, xvi + 269 pages