Introduction -- Parallel Universes and Local Patterns
share
Learning in parallel universes and the mining for local patterns are both relatively new fields of research. Local pattern detection addresses the problem of identifying (small) deviations from an overall distribution of some underlying data in some feature space. Learning in parallel universes on the other hand, deals with the analysis of objects, which are given in different feature spaces, i.e.\ parallel universes; and the aim is on finding groups of objects, which show ``interesting'' behavior in some of these universes. So, while local patterns describe interesting properties of a subset of the overall space or set of objects, learning in parallel universes also aims at finding interesting patterns across different feature spaces or object descriptions. Dagstuhl Seminar~07181 on Parallel Universes and Local Patterns held in May 2007 brought together researchers with different backgrounds to discuss latest advances in both fields and to draw connections between the two.
READ FULL TEXT
