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» Modelling Smooth Paths Using Gaussian Processes
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NIPS
2008
13 years 9 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
CORR
2010
Springer
135views Education» more  CORR 2010»
13 years 7 months ago
A stochastic analysis of greedy routing in a spatially-dependent sensor network
For a sensor network, as tractable spatially-dependent node deployment model is presented with the property that the density is inversely proportional to the sink distance. A stoc...
H. Paul Keeler
CVPR
2008
IEEE
14 years 9 months ago
Unsupervised feature selection via distributed coding for multi-view object recognition
Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem...
Chris Mario Christoudias, Raquel Urtasun, Trevor D...
ICDE
2006
IEEE
194views Database» more  ICDE 2006»
14 years 9 months ago
The Gauss-Tree: Efficient Object Identification in Databases of Probabilistic Feature Vectors
In applications of biometric databases the typical task is to identify individuals according to features which are not exactly known. Reasons for this inexactness are varying meas...
Alexey Pryakhin, Christian Böhm, Matthias Sch...
ISMAR
2007
IEEE
14 years 1 months ago
Initialisation for Visual Tracking in Urban Environments
Outdoor augmented reality systems often rely on GPS to cover large environments. Visual tracking approaches can provide more accurate location estimates but typically require a ma...
Gerhard Reitmayr, Tom Drummond