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» Continuous Gaussian Mixture Modeling
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NN
2006
Springer
15 years 2 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
102
Voted
ICPR
2008
IEEE
15 years 9 months ago
A computer vision based camera pedestal's vertical motion control
. Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking t...
Richard Yi Da Xu, Joshua M. Brown, Jason M. Traish...
NAACL
2003
15 years 4 months ago
Implicit Trajectory Modeling through Gaussian Transition Models for Speech Recognition
It is well known that frame independence assumption is a fundamental limitation of current HMM based speech recognition systems. By treating each speech frame independently, HMMs ...
Hua Yu, Tanja Schultz
127
Voted
MICCAI
2008
Springer
16 years 3 months ago
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...
124
Voted
ICASSP
2011
IEEE
14 years 6 months ago
Dirichlet Mixture Models of neural net posteriors for HMM-based speech recognition
In this paper, we present a novel technique for modeling the posterior probability estimates obtained from a neural network directly in the HMM framework using the Dirichlet Mixtu...
Balakrishnan Varadarajan, Garimella S. V. S. Sivar...