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» A probabilistic framework for semi-supervised clustering
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SOCO
2008
Springer
13 years 7 months ago
A particular Gaussian mixture model for clustering and its application to image retrieval
We introduce a new method for data clustering based on a particular Gaussian mixture model (GMM). Each cluster of data, modeled as a GMM into an input space, is interpreted as a hy...
Hichem Sahbi
CVPR
2006
IEEE
14 years 9 months ago
Unsupervised Bayesian Detection of Independent Motion in Crowds
While crowds of various subjects may offer applicationspecific cues to detect individuals, we demonstrate that for the general case, motion itself contains more information than p...
Gabriel J. Brostow, Roberto Cipolla
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
14 years 1 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
GECCO
2007
Springer
201views Optimization» more  GECCO 2007»
14 years 2 months ago
A parallel framework for loopy belief propagation
There are many innovative proposals introduced in the literature under the evolutionary computation field, from which estimation of distribution algorithms (EDAs) is one of them....
Alexander Mendiburu, Roberto Santana, Jose Antonio...
CLUSTER
2008
IEEE
14 years 2 months ago
Empirical-based probabilistic upper bounds for urgent computing applications
—Scientific simulation and modeling often aid in making critical decisions in such diverse fields as city planning, severe weather prediction and influenza modeling. In some o...
Nick Trebon, Peter H. Beckman