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» A Sampling Method Focusing on Practicality
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PAKDD
2009
ACM
87views Data Mining» more  PAKDD 2009»
15 years 4 months ago
Application-Independent Feature Construction from Noisy Samples
When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on “non-class” attribute noise and we consider how a frequent fault-t...
Dominique Gay, Nazha Selmaoui, Jean-Françoi...
ECCV
2010
Springer
15 years 2 months ago
Practical Autocalibration
As it has been noted several times in literature, the difficult part of autocalibration efforts resides in the structural non-linearity of the search for the plane at infinity. I...
IJCNN
2000
IEEE
15 years 2 months ago
On MCMC Sampling in Bayesian MLP Neural Networks
Bayesian MLP neural networks are a flexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distri...
Aki Vehtari, Simo Särkkä, Jouko Lampinen
COMCOM
2006
87views more  COMCOM 2006»
14 years 9 months ago
Practical utilities for monitoring multicast service availability
Monitoring has become one of the key issues for the successful deployment of IP multicast in the Internet. During the last decade, several tools and systems have been developed to ...
Pavan Namburi, Kamil Saraç, Kevin C. Almero...
TSP
2010
14 years 4 months ago
Distributed sampling of signals linked by sparse filtering: theory and applications
We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin ...
Ali Hormati, Olivier Roy, Yue M. Lu, Martin Vetter...