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» Bayesian Approaches to Gaussian Mixture Modeling
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SDM
2008
SIAM
256views Data Mining» more  SDM 2008»
13 years 10 months ago
Graph Mining with Variational Dirichlet Process Mixture Models
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...
Koji Tsuda, Kenichi Kurihara
TASLP
2010
159views more  TASLP 2010»
13 years 3 months ago
Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each ...
Ngoc Q. K. Duong, Emmanuel Vincent, Rémi Gr...
TASLP
2010
101views more  TASLP 2010»
13 years 3 months ago
Gaussian Model-Based Multichannel Speech Presence Probability
The knowledge of the target speech presence probability in a mixture of signals captured by a speech communication system is of paramount importance in several applications includi...
Mehrez Souden, Jingdong Chen, Jacob Benesty, Sofi&...
BMCBI
2011
13 years 19 days ago
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...
Alfredo A. Kalaitzis, Neil D. Lawrence
NIPS
2003
13 years 10 months ago
Probabilistic Inference in Human Sensorimotor Processing
When we learn a new motor skill, we have to contend with both the variability inherent in our sensors and the task. The sensory uncertainty can be reduced by using information abo...
Konrad P. Körding, Daniel M. Wolpert