BEGIN:VCALENDAR VERSION:2.0 PRODID:ILLC Website X-WR-TIMEZONE:Europe/Amsterdam BEGIN:VTIMEZONE TZID:Europe/Amsterdam X-LIC-LOCATION:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701025T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:/NewsandEvents/Archives/2016/newsitem/7493/4-6 -September-2016-The-26th-International-Conference- on-Inductive-Logic-Programming-ILP-2016-London-Eng land DTSTAMP:20160707T000000 SUMMARY:The 26th International Conference on Induc tive Logic Programming (ILP 2016), London, England DTSTART;VALUE=DATE:20160904 DTEND;VALUE=DATE:20160906 LOCATION:London, England DESCRIPTION:Inductive Logic Programming (ILP) is a subfield of machine learning, which uses logic pr ogramming as a uniform representation technique fo r examples, background knowledge and hypotheses. D ue to its strong representation formalism, based o n first-order logic, ILP provides an excellent mea ns for multi-relational learning and data mining. The ILP conference series, started in 1991, is the premier international forum for learning from str uctured relational data. Originally focusing on th e induction of logic programs, over the years it h as expanded its research horizon significantly and welcomes contributions to all aspects of learning in logic, multi-relational data mining, statistic al relational learning, graph and tree mining, lea rning in other (non-propositional) logic-based kno wledge representation frameworks, exploring inters ections to statistical learning and other probabil istic approaches. For more information, see http ://ilp16.doc.ic.ac.uk Submissions are still open for short papers and papers relevant to the confer ence topics that have been recently published/acce pted for publication by a first-class conference. Submission deadline: 24 July 2016. X-ALT-DESC;FMTTYPE=text/html:
Induc tive Logic Programming (ILP) is a subfield of mach ine learning, which uses logic programming as a un iform representation technique for examples, backg round knowledge and hypotheses. Due to its strong representation formalism, based on first-order log ic, ILP provides an excellent means for multi-rela tional learning and data mining. The ILP conferenc e series, started in 1991, is the premier internat ional forum for learning from structured relationa l data. Originally focusing on the induction of lo gic programs, over the years it has expanded its r esearch horizon significantly and welcomes contrib utions to all aspects of learning in logic, multi- relational data mining, statistical relational lea rning, graph and tree mining, learning in other (n on-propositional) logic-based knowledge representa tion frameworks, exploring intersections to statis tical learning and other probabilistic approaches. \n
\n \n \nFor more inf ormation, see\n http://ilp16.doc.ic.ac.uk \n
Submissions are still open for short papers and papers relevan t to the \n conference topics that have bee n recently published/accepted for \n public ation by a first-class conference.\nSubmission dea dline: 24 July 2016.\n
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