People
- The ML group counts about 30 researchers.
- Special interest groups co-operate and exchange knowledge in a wide range of research topics.
- We are looking for PhD researchers on several research topics. Check out our job offers!
Projects
- The ICON project aims to create a new ICT paradigm, called Inductive Constraint Programming, that bridges the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution of constraint satisfaction problems, but also to employ declarative constraint programming principles in data mining and machine learning.
- GOA Probabilistic logic learning, sometimes also called statistical relational learning, is a newly emerging subfield of artificial intelligence lying at the intersection of knowledge representation, reasoning about uncertainty and machine learning. It aims at combining learning and probabilistic reasoning within first order logic representations.
- More projects
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Publications
Here are some key publications of the ML group:
- Guns, Tias, Nijssen, Siegfried, De Raedt, Luc. Itemset mining: a constraint programming perspective, Artificial Intelligence, 2011.
- Guy Van den Broeck, On the completeness of first-order knowledge compilation for lifted probabilistic inference. NIPS 2011.
- Bernd Gutmann, Ingo Thon, Luc De Raedt: Learning the Parameters of Probabilistic Logic Programs from Interpretations. ECML/PKDD (1) 2011: 581-596
- De Raedt, Luc; Kersting, Kristian; Kimmig, Angelika; Revoredo, Kate; Toivonen, Hannu, Compressing probabilistic Prolog programs, Machine learning, volume 70, issue 2-3, pages 151-168, 2008
- Kersting, Kristian; De Raedt, Luc, Bayesian Logic Programming: Theory and Tool, an Introduction to Statistical Relational Learning, pages 291-322, 2007
- Blockeel, Hendrik; Dehaspe, Luc; Demoen, Bart; Janssens, Gerda; Ramon, Jan; Vandecasteele, Henk, Improving the efficiency of inductive logic programming through the use of query packs, Journal of artificial intelligence research, volume 16, pages 135-166, 2002
- Ramon, Jan; Bruynooghe, Maurice, A polynomial time computable metric between point sets, Acta informatica, volume 37, issue 10, pages 765-780, 2001
- Dzeroski, S; De Raedt, Luc; Driessens, Kurt, Relational reinforcement learning, Machine learning, volume 43, issue 1-2, pages 7-52, 2001
- Kosala, Raymondus; Blockeel, Hendrik, Web mining research : A survey, SIGKDD Explorations - Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data M, volume 2, issue 1, pages 1-15, 2000
- Dehaspe, Luc; Toivonen, H, Discovery of frequent DATALOG patterns, Data mining and knowledge discovery, volume 3, issue 1, pages 7-36, 1999
- Blockeel, Hendrik; De Raedt, Luc, Top-down induction of first-order logical decision trees, Artificial intelligence, volume 101, issue 1-2, pages 285-297, 1998
- Blockeel, Hendrik; De Raedt, Luc; Ramon, Jan, Top-down induction of clustering trees, Proceedings of the 15th International Conference on Machine Learning, pages 55-63, 1998
- Muggleton, Stephen; De Raedt, Luc, Inductive logic programming: theory and methods, Journal of logic programming, volume 19+20, pages 629-679, 1994
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Machine learning is the subfield of artificial intelligence and computer science that studies how machines can learn. A machine learns when it improves its performance on specific tasks with experience. In order to learn, machine learning methods analyze their past experience in order to find useful regularities, which explains why machine learning is closely related to data mining. The machine learning group is investigating all types of machine learning and data mining problems and techniques, though it focuses on dealing with structured data (such as graphs, trees and sequences), symbolic, logical and relational representations, and the use of knowledge and constraints. The group is well-known for its work on inductive logic programming, (statistical) relational learning, relational reinforcement learning, decision tree learning, graph mining, and inductive databases and constraint-based mining. It also studies applications in the life sciences and action- and activity learning.
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