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» Hierarchical mixture models: a probabilistic analysis
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DSP
2007
13 years 7 months ago
Blind separation of nonlinear mixtures by variational Bayesian learning
Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor a...
Antti Honkela, Harri Valpola, Alexander Ilin, Juha...
WWW
2007
ACM
14 years 8 months ago
Topic sentiment mixture: modeling facets and opinions in weblogs
In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously....
Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, Che...
NIPS
2008
13 years 9 months ago
A mixture model for the evolution of gene expression in non-homogeneous datasets
We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in...
Gerald Quon, Yee Whye Teh, Esther Chan, Timothy R....
WSC
1998
13 years 9 months ago
Bayesian Model Selection when the Number of Components is Unknown
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
Russell C. H. Cheng
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 8 months ago
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Affinity functions are the core components in negative selection to discriminate self from non-self. It has been shown that affinity functions such as the r-contiguous distance an...
Thomas Stibor