Computational Intelligence for Knowledge-Based System Design: 13th IPMU Conference, Dortmund, Germany, June 28 - July 2, 2010. Proceedings
Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann
Springer Science & Business Media, Jun 17, 2010 - Computers - 771 pages
The International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the m- agement of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas th between theoreticians and practitioners working in these areas. The 13 IPMU conference took place in Dortmund, Germany, June 28-July 2, 2010. This volumecontains77papersselectedthrougharigorousreviewingprocess among 320 submissions from 36 countries. The contributions re'ect the richness of research in the ?eld of computational intelligence and represent several - portant developments, speci'cally focused on the following sub'elds: (a) machine learning, data mining, and pattern recognition, (b) uncertainty handling, (c) aggregation and fusion of information, (d) logic and knowledge processing. We were delighted that Melanie Mitchell (Portland State University, USA), NihkilR.Pal(IndianStatisticalInstitute),BernhardSch ̈ olkopf(MaxPlanckI- titute for Biological Cybernetics, Tubing ̈ en, Germany) and Wolfgang Wahlster (German Research Center for Arti'cial Intelligence, Saarbruc ̈ ken) accepted our invitationstopresentkeynotelectures.JimBezdekreceivedtheKamp ́edeF ́ eriet Award,grantedeverytwo yearsonthe occasionofthe IPMUconference,in view ofhiseminentresearchcontributionstothehandlingofuncertaintyinclustering, data analysis and pattern recognition.
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aggregation function algorithm analysis application approach artiﬁcial attribute Bayesian Bayesian network belief functions Berlin Heidelberg 2010 binary Choquet integral classiﬁer cluster coherent computed conﬂict consider context copulas corresponding Data Mining data set database decision deﬁned Deﬁnition degree denoted diﬀerent distribution elements equivalent evaluation example expert ﬁnite ﬁrst formal concept formal concept analysis fuzzy logic fuzzy measure Fuzzy Sets given graph H¨ullermeier Heidelberg idempotent IEEE imprecise input interval IPMU kernels Kruse labels learning linguistic LNAI lower previsions method missing values objects obtained operator optimization pairs parameters patterns possibilistic possible preference relation probabilistic probability problem properties proposed Proposition query Rand index random variables regions representation represented rough set rules samples satisﬁes Section Sets and Systems similarity spatial speciﬁc Springer-Verlag Berlin Heidelberg stochastic dominance subset t-norm Theorem theory uncertainty uninorms vector