Publikaties Jan Ramon (2004 - 2007)

a1) Artikels in internationale gereviewde tijdschriften

  1. 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)
  2. J. Ramon, T. Croonenborghs, D. Fierens, H. Blockeel, and M. Bruynooghe, Generalized ordering-search for learning directed probabilistic logical models, Mach. Learn., 2007, accepted. (impactfactor = 3.258)
  3. 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, 2007, accepted. (impactfactor 1.189)
  4. K. Driessens, J. Ramon, and T. Gaertner, Graph kernels and Gaussian processes for relational reinforcement learning, Mach. Learn. 64 (1-3), pp. 91-119, September, 2006. (impactfactor = 3.258)
  5. 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 = 3.258)

c1) Bijdragen op internationale conferenties, volledig gepubliceerd in proceedings

c1.i) In proceedings door professionele uitgevers

  1. 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
  2. J. Ramon, K. Driessens, and T. Croonenborghs, Transfer learning in reinforcement learning problems through partial policy recycling, Proceedings of European Conference on Machine Learning, Warsaw, Poland, 2007, accepted
  3. 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
  4. D. Fierens, J. Ramon, M. Bruynooghe, and H. Blockeel, Learning directed probabilistic logical models: Ordering-search versus structure-search, Proceedings of European Conference on Machine Learning, Warsaw, Poland, 2007, accepted
  5. 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
  6. 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%)
  7. J. Ramon, T. Horváth, L. Schietgat, and S. Wrobel, FOG: Finding outerplanar graphs, Online proceedings fo the Demo Session of the ACM SIGKDD Conference 2006 (Melli, G., ed.), pp. 1-3, 2006
  8. T. Horváth, J. Ramon, and S. Wrobel, Frequent subgraph mining in outerplanar graphs, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Eliassi-Rad, T., ed.), pp. 197-206, 2006
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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

c1.ii) Andere proceedings

  1. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Proceedings of The Sixteenth Annual Machine Learning Conference of Belgium and the Netherlands, Amsterdam, The Netherlands, 2007, accepted
  2. L. Schietgat, J. Ramon, and M. Bruynooghe, A polynomial-time metric for outerplanar graphs, Proceedings of Mining and Learning with Graphs 2007, Florence, Italy, 2007, accepted
  3. 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, accepted
  4. J. Ramon, and S. Nijssen, General graph refinement with polynomial delay, Proceedings of Mining and Learning with Graphs 2007, Florence, Italy, 2007, accepted
  5. C. Vens, J. Ramon, and H. Blockeel, Re-Mauve, a relational model tree learner, ILP'06, 16th International Conference on Inductive Logic Programming, Short Papers (Muggleton, S. and Otero, R., eds.), pp. 216-218, 2006
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  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. F. Güiza, J. Ramon, and H. Blockeel, Gaussian processes for prediction in intensive care, Gaussian Processes in Practice Workshop (Lawrence, N.D. and Schwaighofer, A. and Quinonero, J., eds.), pp. 1-4, 2006
  13. 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
  14. 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
  15. 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
  16. J. Ramon, On the convergence of reinforcement learning using a decision tree learner, Proceedings of the ICML-2005 Workshop on Rich Representations for Reinforcement Learning (Driessens, K. and Fern, A., van Otterlo, M., eds.), pp. 1-6, 2005
  17. 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

c1.iii) Lessen

  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) Abstracten van mededelingen op conferenties, en interne verslagen

  1. 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
  2. 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
  3. 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
  4. 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,
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. T. Horváth, and J. Ramon, Mining d-tenuous outerplanar graphs, Joint APrIL/IQ Workshop, Titisee, Germany, March 15-18, 2006
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. J. Ramon, On the convergence of relational reinforcement learning using a decision tree learner, Freiburg, Leuven and Friends Workshop, FLF'05, Ferričres, Belgium, March 7-9, 2005,
  25. 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
  26. 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
  27. J. Ramon, and J. Struyf, Frequent pattern mining under generalized subsumption, Dutch Belgian Database Day 2004, DBDBD 2004, Antwerpen, Belgium, December 3, 2004