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» Simplifying Mixture Models Using the Unscented Transform
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TNN
2010
216views Management» more  TNN 2010»
12 years 11 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
AUSAI
2004
Springer
13 years 10 months ago
Enhanced Importance Sampling: Unscented Auxiliary Particle Filtering for Visual Tracking
Abstract. The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is...
Chunhua Shen, Anton van den Hengel, Anthony R. Dic...

Presentation
896views
13 years 1 months ago
Exponential families and simplification of mixture models
Presentation of the exponential families, of the mixtures of such distributions and how to learn it. We then present algorithms to simplify mixture model, using Kullback-Leibler di...
ICASSP
2011
IEEE
12 years 8 months ago
A simplified Subspace Gaussian Mixture to compact acoustic models for speech recognition
Speech recognition applications are known to require a significant amount of resources (memory, computing power). However, embedded speech recognition systems, such as in mobile p...
Mohamed Bouallegue, Driss Matrouf, Georges Linares
MICCAI
2009
Springer
14 years 5 months ago
Two-Tensor Tractography Using a Constrained Filter
We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber ori...
James G. Malcolm, Martha Elizabeth Shenton, Yoge...