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» The Spectral Method for General Mixture Models
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ICASSP
2011
IEEE
12 years 11 months ago
Soft frame margin estimation of Gaussian Mixture Models for speaker recognition with sparse training data
—Discriminative Training (DT) methods for acoustic modeling, such as MMI, MCE, and SVM, have been proved effective in speaker recognition. In this paper we propose a DT method fo...
Yan Yin, Qi Li
BMCBI
2010
144views more  BMCBI 2010»
13 years 7 months ago
Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model
Background: Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is gen...
Xin He, Moushumi Sen Sarma, Xu Ling, Brant W. Chee...
JGAA
2007
88views more  JGAA 2007»
13 years 7 months ago
Dynamic Spectral Layout with an Application to Small Worlds
Spectral methods are naturally suited for dynamic graph layout because, usually, moderate changes of a graph yield moderate changes of the layout. We discuss some general principl...
Ulrik Brandes, Daniel Fleischer, Thomas Puppe
ECCV
2006
Springer
13 years 11 months ago
Spatial Segmentation of Temporal Texture Using Mixture Linear Models
In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single tem...
Lee Cooper, Jun Liu, Kun Huang
DAGM
1998
Springer
13 years 12 months ago
Discrete Mixture Models for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co occurrence data. Adoptin...
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann