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» On Fitting Mixture Models
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KDD
2004
ACM
237views Data Mining» more  KDD 2004»
14 years 9 months ago
Bayesian Model-Averaging in Unsupervised Learning From Microarray Data
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are in...
Mario Medvedovic, Junhai Guo
ICASSP
2010
IEEE
13 years 9 months ago
Robust background modeling via standard variance feature
In this paper, a novel standard variance feature is proposed for background modeling in dynamic scenes involving waving trees and ripples in water. The standard variance feature i...
Bineng Zhong, Hongxun Yao, Shaohui Liu
IJAR
2006
98views more  IJAR 2006»
13 years 8 months ago
Inference in hybrid Bayesian networks with mixtures of truncated exponentials
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function can be approximated...
Barry R. Cobb, Prakash P. Shenoy
GIS
2010
ACM
13 years 6 months ago
Probabilistic modeling of traffic lanes from GPS traces
Instead of traditional ways of creating road maps, an attractive alternative is to create a map based on GPS traces of regular drivers. One important aspect of this approach is to...
Yihua Chen, John Krumm
PAMI
2006
215views more  PAMI 2006»
13 years 8 months ago
Bayesian Feature and Model Selection for Gaussian Mixture Models
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...