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» Theory and Use of the EM Algorithm
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ICPR
2008
IEEE
16 years 2 months ago
Component-wise parameter smoothing for learning mixture models
In this paper, we propose a novel component-wise smoothing algorithm that constructs a hierarchy (or family) of smoothened log-likelihood surfaces. Our approach first smoothens th...
Bala Rajaratnam, Chandan K. Reddy
ISBI
2008
IEEE
16 years 2 months ago
A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the traj...
Carl-Fredrik Westin, Lilla Zöllei, Mahnaz Mad...
SPEECH
1998
171views more  SPEECH 1998»
15 years 1 months ago
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framewo...
Nagendra Kumar, Andreas G. Andreou
IJON
2010
109views more  IJON 2010»
14 years 8 months ago
Variational inference for Student-t MLP models
This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for ...
Hang T. Nguyen, Ian T. Nabney
CORR
2011
Springer
174views Education» more  CORR 2011»
14 years 5 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato