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TNN
2010
216views Management» more  TNN 2010»
13 years 4 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
ICML
2007
IEEE
14 years 10 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
TSP
2010
13 years 4 months ago
A robust method to count and locate audio sources in a multichannel underdetermined mixture
We propose a method to count and estimate the mixing directions in an underdetermined multichannel mixture. The approach is based on the hypothesis that in the neighbourhood of som...
Simon Arberet, Rémi Gribonval, Fréd&...
NN
2006
Springer
13 years 10 months ago
Missing data imputation through GTM as a mixture of t-distributions
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural networkinspired, Self-Organizing Maps. The GTM can also ...
Alfredo Vellido
BMVC
2001
14 years 11 days ago
Illumination-Invariant Motion Detection Using Colour Mixture Models
This paper tackles the problem of robust change detection in image sequences from static cameras. Motion cues are detected using frame differencing with an adaptive background est...
Ming Xu, Tim Ellis