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SUMMARY:Clustering Based on Predictive Variances in Gaussian Process Regre
 ssion Models - Dr Hyun-Chul Kim 
DTSTART:20130913T100000Z
DTEND:20130913T110000Z
UID:TALK46876@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:We use predictive variances in Gaussian process regression mod
 el for clustering. The predictive variances in Gaussian processes learned 
 from a training data are shown to comprise an estimate of the support of a
  probability density function. The constructed variance function is then a
 pplied to construct a set of contours that enclose the data points\, which
  correspond to cluster boundaries. To perform clustering tasks of the data
  points\, an associated dynamical system is built\, and its topological in
 variant property is investigated. The experimental results show that the p
 roposed method works successfully for clustering problems with arbitrary s
 hapes.
LOCATION:Engineering Department\, CBL Room BE-438
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