Inductive Logic Programming: 19th International Conference, ILP 2009, Leuven, Belgium, July 2-4, 2010, Revised Papers

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Luc De Raedt
Springer Science & Business Media, Jul 7, 2010 - Mathematics - 257 pages
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The ILP conference series has been the premier forum for work on logic-based approaches to machine learning for almost two decades. The 19th International Conference on Inductive Logic Programming, which was organized in Leuven, July2-4,2009, continuedthistraditionbutalsoreachedouttoothercommunities as it was colocated with SRL-2009 - the International Workshop on Statistical RelationalLearning, andMLG-2009-the7thInternationalWorkshoponMining andLearningwithGraphs. While thesethreeseriesofeventseachhavetheirown focus, emphasis andtradition, they essentiallysharethe problemthatis studied: learning about structured data in the form of graphs, relational descriptions or logic. The colocation of the events was intended to increase the interaction between the three communities. There was a single program with joint invited and tutorial speakers, a panel, regular talks and poster sessions. The invited speakers and tutorial speakers were James Cussens, Jason Eisner, Jure Leskovec, Raymond Mooney, Scott Sanner, and Philip Yu. The panel featured Karsten Borgwardt, Luc De Raedt, Pedro Domingos, Paolo Frasconi, Thomas Gart ] ner, Kristian Kersting, Stephen Muggleton, and C. David Page. Video-recordings of these talks can be found atwww. videolectures. net. The overall program featured 30 talks presented in two parallel tracks and 53 posters. The talks and posters were selected on the basis of an extended abstract. These abstracts can be found at http: // dtai. cs. kuleuven. be/ilp-mlg-srl/. Inaddition, asinpreviousyears, a- lectionofthepapersofILP2009havebeenpublishedinavolumeintheLectures Notes in Arti?cial Intelligence seriesandinaspecialissueoftheMachine Lea- ing Journal.

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Parameter Screening and Optimisation for ILP Using Designed Experiments

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CPLogic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods
Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals
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Sampling for ProbabilisticLogic Sequence Models
Policy Transfer via Markov Logic Networks
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About the author (2010)

Luc De Raedt is currently a full professor (C4) of computer science at the Albert-Ludwigs-University Freiburg and head of the Machine Learning lab. Before coming to Freiburg in 1999, he held positions as (parttime) senior lecturer, lecturer and assistant at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium) and as post-doc of the Fund for Scientific Research, Flanders. He obtained his undergraduate degree as well as his Ph.D. in computer science from the Katholieke Universiteit Leuven (Belgium) in 1986 and 1991. His Ph.D. thesis was subsequently published by Academic Press.

De Raedt has a rich experience in European Union research projects.He (co-)coordinated the successful ESPRIT III and IV Inductive Logic Programming (1 and 2) projects, coordinated the IST assessment project APrIL, and the Marie Curie Training Site DAISY (Foundations of Intelligent Systems). He is at present also involved in the European IST-FET project cInQ belonging to FP5.

De Raedt has (co)-organised several international workshops and conferences.


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