Inductive Logic Programming (Paperback)

13th International Conference, Ilp 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artific #2835)

By Tamas Horvath (Editor), Akihiro Yamamoto (Editor)

Springer, 9783540201441, 406pp.

Publication Date: September 24, 2003

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Description

The13thInternationalConferenceonInductive LogicProgramming(ILP 2003), organizedbytheDepartmentofInformaticsattheUniversityofSzeged, washeld between September 29 and October 1, 2003 in Szeged, Hungary. ILP 2003 was co-located with the Kalm ar Workshop on Logic and Computer Science devoted to the workofL aszloKalm arandto recentresultsinlogicandcomputerscience. This volume contains all full papers presented at ILP 2003, together with the abstracts of the invited lectures by Ross D. King (University of Wales, Aber- twyth) and John W. Lloyd (Australian National University, Canberra). TheILP conferenceseries, startedin1991, wasoriginallydesignedto provide an international forum for the presentation and discussion of the latest research resultsinallareasoflearninglogicprograms.InrecentyearsthescopeofILPhas been broadened to cover theoretical, algorithmic, empirical, and applicational aspects of learning in non-propositional logic, multi-relational learning and data mining, and learning from structured and semi-structured data. The program committee received altogether 58 submissions in response to the call for papers, of which 5 were withdrawn by the authors themselves. Out of the remaining 53 submissions, the program committee selected 23 papers for full presentation at ILP 2003. High reviewing standards were applied for the selection of the papers. For the ?rst time, the "Machine Learning" journal awarded the best student papers. The awards were presented to Marta Arias for her theoretical paper withRoniKhardon: ComplexityParametersforFirst-OrderClasses, andtoKurt DriessensandThomasG] artnerfortheirjointalgorithmicpaperwithJanRamon: Graph Kernels and Gaussian Processes for Relational Reinforcement Learning.