17 search results - page 2 / 4 » Nonlinear random effects mixture models: Maximum likelihood ... |
BMEI
2009
IEEE
13 years 6 months ago
2009
IEEE
The Gaussian mixture model is a powerful statistical tool in data modeling and analysis. Generally, the EM algorithm is utilized to learn the parameters of the Gaussian mixture. Ho...
NIPS
1998
13 years 6 months ago
1998
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
CAIP
1999
Springer
13 years 9 months ago
1999
Springer
This paper casts the problem of point-set alignment via Procrustes analysis into a maximum likelihood framework using the EM algorithm. The aim is to improve the robustness of the...
ICDM
2006
IEEE
13 years 11 months ago
2006
IEEE
In spite of the initialization problem, the ExpectationMaximization (EM) algorithm is widely used for estimating the parameters in several data mining related tasks. Most popular ...
CIVR
2006
Springer
13 years 9 months ago
2006
Springer
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...