Inductive Logic Programming: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010, Revised Papers
Paolo Frasconi, Francesca A. Lisi
Springer Science & Business Media, May 23, 2011 - Computers - 278 pages
This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.
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accuracy action advice Aleph algorithm annotation applied approach Artificial Intelligence atoms background knowledge Berlin Heidelberg 2011 binary refinement compute concept constraint continuous facts dataset defined Definition denoted distribution domain evaluate experiments F.A. Lisi Eds first-order first-order logic FOAF frame problem framework Frasconi and F.A. function gene Gibbs sampling goal graph Heidelberg heuristic horn clauses HYPER/N hypothesis iLogCHEM ILP system implemented Inductive Logic Programming inference input k-best language layer literals LNCS LNAI Machine Learning meta-level abduction method Muggleton multi-class multi-instance negative examples nodes Ondex paper parameter patterns plans polygon positive examples prediction probabilistic probabilistic logic probability probability distribution problem Progol ProGolem Prolog property items protein query Raedt random heuristic search relational learning represent RoboCup rule learning sample search space Section spatial Springer statistical units stochastic refinement search structure subset subsumption SWI-Prolog target theory training examples variables