Universiteit Leiden

nl en

Hendrik Blockeel

Onderzoeker / gast

Naam
Dr.ir. H.L.W. Blockeel
Telefoon
+31 71 527 5778
E-mail
h.l.w.blockeel@liacs.leidenuniv.nl

Hendrik Blockeel (PhD in Computer Science, 1998, Katholieke Universiteit Leuven) is a professor ("hoogleraar") at the Katholieke Universiteit Leuven, and part-time associate professor ("universitair hoofddocent") at the University of Leiden, The Netherlands. His research interests include theory and algorithms for machine learning and data mining in general, with a particular focus on relational learning, graph mining, probabilistic logics, inductive knowledge bases, and applications of these techniques in the broader field of computer science, bio-informatics, and medical informatics.

Onderzoeker / gast

  • Wiskunde en Natuurwetenschappen
  • Leiden Inst Advanced Computer Sciences

Werkadres

Snellius
Niels Bohrweg 1
2333 CA Leiden
Kamernummer 123

Contact

  • Rahmani H., Blockeel H.L.W. & Bender A. (2015), Using a human disease network for augmenting prior knowledge about diseases, Intelligent Data Analysis 19(4): 897-916.artikel in tijdschrift (refereed)
  • Blockeel H.L.W. (2015), Data Mining: From Procedural to Declarative Approaches, New Generation Computing 33(2): 115-135.artikel in tijdschrift (refereed)
  • Bruynooghe M., Blockeel H.L.W., Bogaerts B., Cat B. de, Pooter S. De, Jansen J., Labarre A., Ramon J., Denecker M. & Verwer S. (2015), Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3, Theory and Practice of Logic Programming 15(6): 783-817.artikel in tijdschrift (refereed)
  • Becerra-Bonache L., Blockeel H.L.W., Galván M. & Jacquenet F. (2015), A First-Order-Logic Based Model for Grounded Language Learning. In: Proceedings Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015. nr. LNCS 9385. 49-60.congresbijdrage (refereed)
  • Verbeeck D. & Blockeel H.L.W. (2015), Slower Can Be Faster: The iRetis Incremental Model Tree Learner. In: Proceedings Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015. nr. LNCS 9385. 322-333.congresbijdrage (refereed)
  • Craenendonck T. van & Blockeel H.L.W. (2015), Limitations of using constraint set utility in semi-supervised clustering. In: Vanschoren, J., Brazdil P., Giraud-Carrier C., Kotthoff L. (red.) Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel 2015). nr. CEUR Workshop Proceedings 1455. Aachen, Germany: CEUR-WS. 27-42.congresbijdrage (refereed)
  • Adam A. & Blockeel H.L.W. (2015), Dealing with Overlapping Clustering: A Constraint-based Approach to Algorithm Selection. In: Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel 2015). nr. CEUR Workshop Proceedings 1455. 43-54.congresbijdrage (refereed)
  • Blockeel H.L.W., Leeuwen M. van & Vinciotti V. (Red.) (2014), Advances in Intelligent Data Analysis XIII - 13th International Symposium, IDA 2014 nr. LNCS 8819: Springer.boekredactie
  • Dumancic S., Adam A. & Blockeel H.L.W. (2014), Learning symbolic features for rule induction in computer aided diagnosis. In: Proceedings International Conference on Inductive Logic Programming.congresbijdrage (refereed)
  • Vanwinckelen G. & Blockeel H.L.W. (2014), A meta-learning system for multi-instance classification. In: Proceedings , International Workshop on Learning over Multiple Contexts.congresbijdrage (refereed)
  • Vanwinckelen G. & Blockeel H.L.W. (2014), Some insights into learner evaluation with cross-validation. In: Proceedings ECML/PKDD Workshop on Statistically Sound Data Mining.congresbijdrage (refereed)
  • Verbeeck, D. & Blockeel H.L.W. (2014), Sensitivity analysis of search-space dimensionality on recent multi-objective evolutionary algorithms. In: Proceedings 23rd Annual Belgian-Dutch Conference on Machine Learning (BENELEARN). 48-54.congresbijdrage (refereed)
  • Blockeel H, Kersting K, Nijssen S & Zelezný F (Red.) (2013), Proceedings Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III Lecture Notes in Computer Science nr. 8190: Springer-Verlag.boekredactie
  • Blockeel H, Kersting K, Nijssen S & Zelezný F (Red.) (2013), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II Lecture Notes in Computer Science nr. 8189: Springer-Verlag.boekredactie
  • Blockeel H, Kersting K, Nijssen S & Zelezný F (Red.) (2013), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I Lecture Notes in Computer Science nr. 8188: Springer-Verlag.boekredactie
  • Shamsinejadbabaki P., Saraee M. & Blockeel H.L.W. (2013), Causality-based cost-effective action mining, Intelligent Data Analysis 17(6): 1075-1091.artikel in tijdschrift (refereed)
  • Taghipour N., Fierens D., Davis J. & Blockeel H.L.W. (2013), Lifted Variable Elimination: Decoupling the Operators from the Constraint Language, Journal of Artificial Intelligence Research 47: 393-439.artikel in tijdschrift (refereed)
  • Brijder R. & Blockeel H.L.W. (2013), On the inference of non-confluent NLC graph grammars, Journal of Logic and Computation 23(4): 799-814.artikel in tijdschrift (refereed)
  • Taghipour N., Fierens D., Broek G. Van den, Davis J. & Blockeel H.L.W. (2013), On the Completeness of Lifted Variable Elimination. In: Proceedings AAAI Workshop Statistical Relational Artificial Intelligence 2013.congresbijdrage (refereed)
  • Taghipour N., Fierens D., Broek G. Van den, Davis J. & Blockeel H.L.W. (2013), Completeness Results for Lifted Variable Elimination. In: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2013. 572-580.congresbijdrage (refereed)
  • Hu P., Vens C., Verstrynge B. & Blockeel H.L.W. (2013), Generalizing from Example Clusters. In: Proceedings of Discovery Science 2013. 64-78.congresbijdrage (refereed)
  • Verbeeck D., Maes F., Grave K. De & Blockeel H.L.W. (2013), Multi-objective optimization with surrogate trees. In: Proceedings Genetic and Evolutionary Computation Conference, GECCO '13: ACM. 679-686.congresbijdrage (refereed)
  • Costa E.P., Verwer S. & Blockeel H.L.W. (2013), Estimating Prediction Certainty in Decision Trees. In: Proceedings Advances in Intelligent Data Analysis XII - 12th International Symposium, IDA 2013: Springer-Verlag. 138-149.congresbijdrage (refereed)
  • Taghipour N., Davis J. & Blockeel H.L.W. (2013), First-order Decomposition Trees. In: Proceedings Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. 1052-1060.congresbijdrage (refereed)
  • Adam A., Blockeel H.L.W., Govers S. & Aertsen A. (2013), SCCQL : A Constraint-Based Clustering System. In: Proceedings Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013: Springer-Verlag. 681-684.congresbijdrage (refereed)
  • Blockeel H.L.W. (2013), Statistical Relational Learning. In: Handbook on Neural Information Processing 2013: Springer-Verlag. 241-281.boekdeel
  • Blockeel H.L.W., Kersting K., Nijssen S.G.R. & Zelezny F. (Red.) (2013), Data Mining Knowledge Discovery, Special Issue of the ECML PKDD 2013 journal track nr. 27(3).boekredactie
  • Blockeel H.L.W., Kersting K., Nijssen S.G.R. & Zelezny F. (Red.) (2013), Machine Learning, special issue of the ECML PKDD 2013 journal track nr. 93(1).boekredactie
  • Taghipour N., Fierens D., Davis J. & Blockeel H. (2012), Lifted Variable Elimination with Arbitrary Constraints, Journal of Machine Learning Research Pr. Tr. 22: 1194-1202.artikel in tijdschrift (refereed)
  • Meert W., Broeck G. van den, Taghipour N., Fierens D., Blockeel H., Davis J. & De Raedt L. (2012), Lifted inference for probabilistic programming. In: Proceedings of the NIPS Probabilistic Programming Workshop.congresbijdrage (refereed)
  • Taghipour N., Fierens D., Broeck G. van den, Davis J. & Blockeel H. (2012), Lifted variable Elimination: A Novel Operator and Completeness Results.congresbijdrage (refereed)
  • Piccart B., Blockeel H., Georges A. & Eeckhout L. (2012), Predictive learning in two-way datasets. Latest advances in inductive logic programming. In: International Conference on Inductive Logic Programming.congresbijdrage (refereed)
  • Blockeel H., Kersting K., Nijssen S. & Zelezny F. (2012), A Revised Publication Model for ECML PKDD. .: ArXiv.rapport
  • Andrews T., Blockeel H., Bogaerts H., Bruynooghe M., Denecker M., Pooter S. De, Mace C. & Ramon J. (2012), Analyzing manuscript traditions using constraint-based data mining. In: First Workshop on Combining Constraint Solving with Mining and Learning.congresbijdrage (refereed)
  • Blockeel H., Calders T., Fromont E., Goethals B., Prado A. & Robardet C. (2012), An inductive database system based on virtual mining views, Journal on Data Mining Knowledge Discovery 24(1): 247-287.artikel in tijdschrift (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2012), Predicting Genes Involved in Human Cancer Using Network Contextual Information, Journal Integrative Bioinformatics 9(1).artikel in tijdschrift (refereed)
  • Blockeel H., Bogaerts B., Bruynooghe M., Cat B. de & Pooter S. De (2012), Modeling Machine Learning and Data Mining Problems with FO(.). In: Proceedings of the 28th International Conference on Logic Programming - Technical Communications (ICLP'12). 14-25.congresbijdrage (refereed)
  • Maervoet J., Vens C., Berghe G. Van den, Blockeel H. & Causmaecker P. De (2012), Outlier detection in relational data: A case study in geographical information systems, Expert Systems with Applications 39(5): 4718-4728.artikel in tijdschrift (refereed)
  • Driessens K., Vanwinckelen G. & Blockeel H. (2012), Meta-learning from an experiment database. In: ICML Workshop on Teaching Machine Learning.congresbijdrage (refereed)
  • Vanschoren J., Blockeel H., Pfahringer B. & Holmes G. (2012), Experiment databases, A new way to share, organize and learn from experiments, Machine Learning 87: 127-158.artikel in tijdschrift (refereed)
  • Vanwinckelen G. & Blockeel H. (2012), On estimating model accuracy with repeated cross-validation. In: BeneLearn 2012: Proceedings of the 21st Belgian-Dutch Conference on Machine Learningg (BeneLearn). 39-44.congresbijdrage (refereed)
  • Putten P.W.H. van der, Veenman C., Vanschoren J., Israel M. & Blockeel H. (Red.) (2011), Proceedings of the 20th Annual Belgian-Dutch Conference on Machine Learning (BENELEARN 2011). The Hague: Universiteit Leiden.boekredactie
  • Witsenburg M. & Blockeel H. (2011), K-means based approaches to clustering nodes in annotated graphs. In: Proceedings International Symposium on Methodologies for Intelligent Systems Lecture Notes in Computer Science: Springer.congresbijdrage (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2011), Collaboration-Based Function Prediction in Protein-Protein Interaction Networks. In: Advances in Intelligent Data Analysis X - 10th Internationl Symposium Lecture notes in Computer Science: Springer. 318-327.congresbijdrage (refereed)
  • Kok J.N., Knobbe A., Blockeel H., Obladen B. & Koenders E. (2011), Large Data Stream Processing for Bridge Management Systems. In: Proceedings First Middle East Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures (SMAR 2011).congresbijdrage (refereed)
  • Blockeel H. (2011), Hypothesis language. In: Sammut C., Webb G. (Red.) Encyclopedia of Machine Learning: Springer. 507-511.boekdeel
  • Tragante O. do, Fierens D. & Blockeel H. (2011), Instance-level accuracy versus bag-level accuracy in multi-instance learning. In: Proceedings 22nd Benelux Conference on Artificial Intelligence (BNAIC).congresbijdrage (refereed)
  • Blockeel H., Borgwardt K., De Raedt L., Domingos P., Kersting K. & Yan X. (2011), Guest editorial to the special issue on inductive logic programming mining and learning in graphs and statistical relational learning, Machine Learning 83: 133-135.artikel in tijdschrift
  • Ray S., Scott S. & Blockeel H. (2011), Multi-instance learning. In: Sammut C., Webb G. (Red.) Encyclopedia of Machine Learning: Springer. 701-710.boekdeel
  • Blockeel H., Calders T., Fromont E., Goethals B., Prado A. & Robardet C. (2011), Inductive querying with virtual mining views. In: Dzeroski S., Goethals B., Panov P. (Red.) Inductive Databases and Queries: Constraint-Based Data Mining: Springer. 265-287.boekdeel
  • Uwents W., Monfardini G., Blockeel H., Gori M. & Scarselli F. (2011), Neural Networks for Relational Learning: An experimental comparison, Machine Learning 82(3): 315-349.artikel in tijdschrift (refereed)
  • Struyf J. & Blockeel H. (2011), Relational learning. In: Sammut C., Webb G. (Red.) Encyclopedia of Machine Learning: Springer. 851-857.boekdeel
  • Paula Costa E. De, Vens C. & Blockeel H. (2011), Protein Subfamily Identification using Clustering Trees, Proceedings 20th Belgian Dutch Conference on Machine Learning (BENELEARN) : 105-106.abstract
  • Blockeel H. (2011), Bias Specification Language. In: Encyclopedia of Machine Learning 1. 90-100.boekdeel
  • Brijder R. & Blockeel H. (2011), Characterizing compressibility of disjoint subgraphs with NLC grammars. In: Proceedings Fifth International conference on Language and Automata Theory and Applications (LATA 2011) Lecture notes in Computer Science. 167-178.congresbijdrage (refereed)
  • Blockeel H. (2011), Hypothesis Space. In: Encyclopedia of Machine Learning 1. 511-513.boekdeel
  • Blockeel H., Calders T., Fromont E., Goethals B., Prado A. & Robardet C. (2011), A practical comparative study of data mining query languages. In: Dzeroski S., Goethals B., Panov P. (Red.) Inductive Databases and Constraint-Based Data Mining: Springer. 59-77.boekdeel
  • Paula Costa E. De, Vens C. & Blockeel H. (2011), Identification and Classification of Protein Subfamilies using To-Down Phylogenetic Tree Reconstruction, Proceedings European Conference on Computational Biology .abstract
  • Rahmani H., Blockeel H. & Bender A. (2011), Interaction-based feature selection for predicting cancer-related proteins in protein-protein interaction networks Proceedings Fifth International Workshop on Machine Learning in System Biology.congresbijdrage (refereed)
  • Blockeel H. (2011), Statistical relational learning. In: Bianchini M., Maggini M., Jain L. (Red.) Handbook of Neural Information Processing.boekdeel
  • Blockeel H. (2011), Observation Language. In: Encyclopedia of Machine Learning 1. 733-735.boekdeel
  • Piccart B., Georges A., Blockeel H. & Eeckhout L. (2011), Ranking Commercial Machines Through Data Transposition. In: Proceedings IEEE International Symposium on Workload Characterization (IISWC). 3-14.congresbijdrage (refereed)
  • Witsenburg M. & Blockeel H. (2011), Improving the accuracy of similarity measures by using link information. In: Proceedings International Symposium on Methodologies for Intelligent Systems Lecture notes in Computer Science: Springer.congresbijdrage (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2010), Collaboration based function prediction in protein-protein interaction networks, Proceedings of the 7th International Symposium on Networks in Bioinformatics .abstract
  • Fierens D., Ramon J., Blockeel H. & Bruynooghe M. (2010), A comparison of pruning criteria for probability trees, Machine Learning 78: 251-285.artikel in tijdschrift (refereed)
  • Wiele T. Van de, Tragante Do O.V., Silva R. Alves da & Blockeel H. (2010), Knowledge discovery in panoramic X-rays for postmortem identification. In: Proceedings of BNAIC. Benelux Conference on Artificial Intelligence. 1-6.congresbijdrage (refereed)
  • Rahmani H., Nobakht B. & Blockeel H. (2010), Collaboration-based social tag prediction in the graph of annotated web pages DyNaK 2010: Dynamic Networks and Knowledge Discovery, Proceedings of the 9th European Conference on Computational Biology : 1-12.abstract
  • Meert W., Taghipour N. & Blockeel H. (2010), First-order Bayes-ball. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010) Lecture Notes in Computer Science: Springer.congresbijdrage (refereed)
  • Knobbe A., Blockeel H., Koopman A., Calders T., Obladen Bas, Bosma C., Galenkamp H., Koenders E. & Kok J.N. (2010), InfraWatch: Data management of large systems for monitoring infrastructural performance. In: Intelligent Data Analysis (IDA) Lecture notes in Computer Science: Springer. 91-102.congresbijdrage (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2010), Collaboration-based function prediction in protein-protein interaction networks. In: Machine Learning in Systems Biology: Proceedings of the Fourth International Workshop. 55-58.congresbijdrage (refereed)
  • Paula Costa E. De, Vens C. & Blockeel H. (2010), Reconstructing phylogenetic trees from clustering trees, Proceedings European Conference on Computational Biology .abstract
  • Blockeel H., Piccart B., Rahmani H. & Fierens D. (2010), Three complementary approaches to context aware movie recommendation. In: Proceedings of the Workshop on Context-Aware Movie Recommendation: ACM. 57-60.congresbijdrage (refereed)
  • Uwents W., Monfardini G., Blockeel H., Gori M. & Scarselli F. (2010), Neural networks for relational learning: An experimental comparison, Machine Learning.artikel in tijdschrift (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2010), Predicting the functions of proteins in protein-protein interaction networks from global information. In: JMLR: Workshop and Conference Proceedings: International Workshop on Machine Learning in Systems Biology. 82-97.congresbijdrage (refereed)
  • Vens C., De Paula Costa E. & Blockeel H. (2010), Top-down induction of phylogenetic trees. In: European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics Lecture Notes in Computer Science: Springer. 62-73.congresbijdrage (refereed)
  • Loon K. Van, Guiza Grandas F., Meyfroidt G., Aerts J., Ramon J., Blockeel H., Bruynooghe M., Berghe G. Van den & Berckmans D. (2010), Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis, Journal of Medical Systems 34(3): 229-239.artikel in tijdschrift (refereed)
  • Blockeel H. & Witsenburg M. (2010), Exploiting homophily in unsupervised learning, Lorentz Workshop on Mining Patterns and Subgroups .abstract
  • Rahmani H., Blockeel H. & Bender A. (2010), Collaboration-based function prediction in protein-protein interaction networks, Proceedings European Conference on Computational Biology .abstract
  • Blockeel H., Rahmani H. & Witsenburg M. (2010), On the importance of similarity measures for planning to learn. In: Brazdil P., Bernstein A., Kietz J. (Red.) 19th European Conference on Artificial Intelligence, 3rd Planning to Learn workshop (PlanLearn-2010). 69-74.congresbijdrage (refereed)
  • Ray S., Scott S. & Blockeel H. (2010), Multi-instance learning. In: C. Sammut G. Webb (Red.) Encyclopedia of Machine Learning: Springer.boekdeel
  • Vanschoren J. & Blockeel H. (2010), Experiment databases Inductive Databases and Queries: Constraint-based Data Mining. In: Dzeroski S., Goethals B., Panov P. (Red.) Inductive Databases and Queries: Constraint-based Data Mining: Springer.boekdeel
  • Meert W., Struyf J. & Blockeel H. (2010), Contextual variable elimination with overlapping contexts. In: Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM2010). 193-201.congresbijdrage (refereed)
  • Schietgat L., Vens C., Struyf J., Blockeel H., Kocev D. & Dzeroski S. (2010), Predicting gene function using hierarchical multi-label decision tree ensembles, BMC Bioinformatics 11.artikel in tijdschrift (refereed)
  • Taghipour N., Fierens D. & Blockeel H. (2010), Probabilistic logical learning for biclustering: A case study with surprising results: K.U. Leuven.rapport
  • Vens C., Schietgat L., Struyf J., Blockeel H., Kocev D. & Dzeroski S. (2010), Predicting gene functions using predictive clustering trees Inductive Databases and Queries: Constraint-based Data Mining. In: Dzeroski S., Goethals B., Panov P. (Red.) Inductive Databases and Queries: Constraint-based Data Mining: Springer.boekdeel
  • Vanschoren J. & Blockeel H. (2009), Stand on the shoulders of giants. Towards a portal for collaborative experimentation in data mining. In: Proceedings of the SoKD-09 International Workshop on Third Generation Data Mining at ECML PKDD 2009. 88-99.congresbijdrage (refereed)
  • Verschoren J. & Blockeel H. (2009), A community-based platform for machine learning experimentation. In: Machine Learning and Knowledge Discovery in Databases, European Conference ECML PKDD 2009 Lecture notes in Computer Science: Springer. 750-754.congresbijdrage (refereed)
  • Meert W., Struyf J. & Blockeel H. (2009), CP-logic theory inference with contextual variable elimination and comparison to BDD based inference methods. In: Proceedings of the 19th International Conference on Inductive Logic Programming (ILP).congresbijdrage (refereed)
  • Blockeel H. & Brijder R. (2009), Learning non-confluent NLC graph grammar rules. In: Mathematical Theory and Computational Practice, Fifth Conference on Computability in Europe, CiE 2009: University of Heidelberg. 60-69.congresbijdrage (refereed)
  • Rahmani H., Blockeel H. & Bender A. (2009), Predicting the functions of proteins in PPI networks from global information. In: Proceedings of the Third International Workshop on Machine Learning in Systems Biology: Helsinki University Printing House. 85-94.congresbijdrage (refereed)
  • Loon K. Van, Guiza Grandas F., Meyfroidt G., Aerts J.-M., Ramon J., Blockeel H., Bruynooghe M., Van den Berghe G. & Berckmans D. (2009), Dynamic Data Analysis and Data Mining for Prediction of Clinical Stability. In: Medical Informatics Europe, edition 22. 590-594.congresbijdrage (refereed)
  • Paula Costa E. De, Vens C. & Blockeel H. (2009), Top-down phylogenetic tree reconstruction: a decision tree approach. In: International Workshop on Machine Learning in Systems Biology.congresbijdrage (refereed)
  • Schietgat L., Theys K., Ramon J., Blockeel H. & Vandamme A. (2008), Distinguishing epidemiological dependent from treatment (resistance) dependent HIV mutations: problem statement. In: Proceedings of the 1st International Workshop on Statistical and Relational Learning in Bioinformatics.congresbijdrage (refereed)
  • Vanschoren J., Blockeel H., Pfahringer B. & Holmes G. (2008), Experiment databases: Creating a new platform for meta-learning research. In: Proceedings of the ICML/COLT/UAI. 10-15.congresbijdrage (refereed)
  • Witsenburg M. & Blockeel H. (2008), A method to extend existing document clustering procedures in order to include relational information. In: Kaski, S., Vishwanathan, V., Wrobel, S. (Red.) 6th International Workshop on Mining and Learning With Graphs. 1-3.congresbijdrage (refereed)
  • Drouillon P. & Blockeel H. (2008), Prediction of molecular substructures from mass spectrograms using constraint based clustering. In: Kaski, S., Vishwanathan, S., Wrobel, S. (Red.) 6th International Workshop on Mining and Learning With Graphs. 1-4.congresbijdrage (refereed)
  • Blockeel H. & Nijssen S. (2008), Induction of node label controlled graph grammar rules. In: Kaski, S., Vishwanathan, S., Wrobel, S. (Red.) 6th International Workshop on Mining and Learning with Graphs. 1-4.congresbijdrage (refereed)
  • Blockeel H., Calders T., Fromont E., Goethals B. & Prado A. (2008), Mining Views: Database Views for Data Mining. In: IEEE 24th International Conference on Data Engineering. 1608-1611.congresbijdrage (refereed)
  • Meert W., Struyf J. & Blockeel H. (2008), Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques, Fundam. Inform. 89(1): 131-160.artikel in tijdschrift (refereed)
  • Blockeel H., Calders T., Fromont E., Goethals B., Prado A. & Robardet C. (2008), An Inductive Database Prototype Based on Virtual Mining Views. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1061-1064.congresbijdrage (refereed)
  • Schietgat L., Ramon J., Bruyooghe M. & Blockeel H. (2008), An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules. In: Discovery Science. Berlin / Heidelberg: Springer. 197-209.boekdeel
  • Vanschoren J. & Blockeel H. (2008), Investigating classifier learning behavior with experiment databases. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (Red.) Data Analysis, Machine Learning and Applications. Berlin Heidelberg: Springer. 421-428.boekdeel
  • Uwents W. & Blockeel H. (2008), Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach. In: Inductive Logic Programming. Berlin / Heidelberg: Springer. 315-329.boekdeel
  • Piccart B., Struyf J. & Blockeel H. (2008), Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees. In: Discovery Science. Berlin / Heidelberg: Springer. 64-75.boekdeel
  • Meert W., Struyf J. & Blockeel H. (2008), Learning ground CP-logic theories by leveraging Bayesian network learning technique, Fundamenta informaticae.artikel in tijdschrift (refereed)
  • Blockeel H. (2008), Exposing the Causal Structure of Processes by Learning CP-Logic Programs. In: PRICAI 2008. 2.congresbijdrage (refereed)
  • Vanschoren J., Blockeel H., Pfahringer B. & Holmes G. (2008), Organizing the World's Machine Learning Information. In: . ISoLA 2008. 693-708.congresbijdrage (refereed)
  • Uwents W. & Blockeel H. (2008), A Comparison between Neural Network Methods for Learning Aggregate Functions. In: Discovery Science. Berlin / Heidelberg: Springer. 88-99.boekdeel
  • Vens C., Struyf J., Schietgat L., Dzeroski S. & Blockeel H. (2008), Decision trees for hierarchical multi-label classification, Machine Learning 73(2): 185-214.artikel in tijdschrift (refereed)
  • Vanschoren J., Blockeel H., Pfahringer B. & Holmes G. (2008), Organizing the World’s Machine Learning Information. In: Leveraging Applications of Formal Methods, Verification and Validation. Berlin / Heidelberg: Springer. 693-708.boekdeel
  • Blockeel H., Shavlik J.W. & Tadepalli P. (2008), Guest editors' introduction: special issue on inductive logic programming, Machine Learning 73(1): 1-2.artikel in tijdschrift (refereed)
  • Blockeel H., Shavlik J.W. & Tadepalli P. (2008), Guest editors’ introduction: Special issue on inductive logic programming, Machine Learning 73(1): 1-2.artikel in tijdschrift (refereed)
  • Ramon J., Croonenborghs T., Fierens D., Blockeel H. & Bruynooghe M. (2008), Generalized ordering-search for learning directed probabilistic logical models, Machine Learning 70(2-3): 169-188.artikel in tijdschrift (refereed)
  • Fromont E., Blockeel H. & Struyf J. (2007), Integrating decision tree learning into inductive databases, Knowledge Discovery in Inductive Databases. In: Dzeroski, S., Struyf, J. (Red.) 5th International Workshop, KDID 2006, Berlin, Germany, September 18, 2006, Revised Selected and Invited Papers Lecture notes in Computer Science. 81-96.congresbijdrage (refereed)
  • Vanschoren J. & Blockeel H. (2007), Investigating classifier learning behavior with experiment databases. In: Proceedings of The 31st Annual Conference of the German Classification Society on Data Analysis, Machine Learning, and Applications. Freiburg.congresbijdrage (refereed)
  • Ramon J., Croonenborghs T., Fierens D., Blockeel H. & Bruynooghe M. (2007), Generalized ordering-search for learning directed probabilistic logical models. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (Red.) Inductive Logic Programming, ILP 2006, Revised Selected Papers Lecture notes in Computer Science. 40-42.congresbijdrage (refereed)
  • Assche A. van & Blockeel H. (2007), Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble. In: Kok, J., Koronacki, J., López de Mántaras, R., Matwin, S., Mladenic, D., Skowron, A. (Red.) Machine Learning: ECML Lecture notes in Computer Science: Springer. 418-429.congresbijdrage (refereed)
  • Blockeel H., Witsenburg M. & Kok J.N. (2007), Hypergraphs, and Inductive Logic Programming. In: Paolo Frasconi, Kristian Kersting, Koji Tsuda (Red.) Mining and Learning with Graphs. 93-96.congresbijdrage (refereed)
  • Vens C., Ramon J. & Blockeel H. (2007), ReMauve, a relational model tree learner. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A (Red.) Inductive Logic Programming Lecture notes in Computer Science. 424-438.congresbijdrage (refereed)
  • Ramon J., Fierens D., Güiza F., Meyfroidt G. & Blockeel H. (2007), Mining data from intensive care patients, Advanced Engineering Informatics 21(3): 243-256.artikel in tijdschrift (refereed)
  • Fierens D., Ramon J., Bruynooghe M. & Blockeel H. (2007), Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search. In: Kok, J.N., Koronacki, J., López de Mántaras, R., Matwin, S., Mladenic, D., Skowron, A. (Red.) Machine Learning: ECML 2007, 18th European Conference on Machine Learning Lecture notes in Computer Science: Springer. 567-574.congresbijdrage (refereed)
  • Blockeel H., Ramon J., Shavlik J.W. & Tadepalli P. (2007), Inductive Logic Programming. Berlin: Springer.boek
  • Vanschoren J., Assche A. van, Vens C. & Blockeel H. (2007), Meta-learning from experiment databases: An illustration. In: Someren, M. van, Katrenko, S., Adriaans, P. (Red.) Machine Learning Conference of Belgium and The Netherlands. 120-127.congresbijdrage (refereed)
  • Croonenborghs T., Ramon J., Blockeel H. & Bruynooghe M. (2007), Online Learning and Exploiting Relational Models in Reinforcement Learning. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence. 726-731.congresbijdrage (refereed)
  • Meert W., Struyf J. & Blockeel H. (2007), Learning ground CP-logic theories by means by Bayesian network techniques. In: Malerba, D., Appice, A., Ceci, M. (Red.) Proceedings of the 6th International Workshop on Multi-Relational Data Mining. 93-104.congresbijdrage (refereed)
  • Hoste K., Eeckhout L. & Blockeel H. (2007), Analyzing commercial processor performance numbers for predicting performance of applications of interest. In: Golubchik, L., Ammar, M.H., Harchol-Balter, M. (Red.) Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. San Diego: ACM. 375-376.congresbijdrage (refereed)
  • Blockeel H. & Meert W. (2007), Towards learning non-recursive LPADs by transforming them into Bayesian networks. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (Red.) Inductive Logic Programming, ILP 2006, Revised Selected Papers Lecture notes in Computer Science. 94-108.congresbijdrage (refereed)
  • Blockeel H. & Vanschoren J. (2007), Experiment databases: Towards an improved experimental methodology in machine learning. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenic, D., Skowron, A. (Red.) Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases Lecture notes in Computer Science. 6-17.congresbijdrage (refereed)
  • Ramon J., Dubrovskaya S. & Blockeel H. (2007), Learning resistance mutation pathways of HIV. In: Proceedings of The Sixteenth Annual Machine Learning Conference of Belgium and the Netherlands.congresbijdrage (refereed)
  • Blockeel H., Calders T., Fromont E., Goethals B. & Prado A. (2007), Mining views: Database views for data mining. In: Nijssen, S. and De Raedt, L. (Red.) Proceedings of the 1st International Workshop on Constraint-Based Mining and Learning. 21-33.congresbijdrage (refereed)
  • Vens C. & Blockeel H. (2007), Using clustering trees for learning phylogenetic trees. In: Frasconi, P., Kersting, K., Tsuda, K., (Red.) Proceedings of the 5th International Workshop on Mining and Learning with Graphs. 143-146.congresbijdrage (refereed)
  • Drouillon P. & Blockeel H. (2007), Prediction of molecular substructures from mass spectograms. In: Frasconi, P., Kersting, K., Tsuda, K. (Red.) Prediction of molecular substructures from mass spectograms. 171-174.congresbijdrage (refereed)
  • KU Leuven hoogleraar
  • KU Leuven hoogleraar