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» Learning Mixtures of Gaussians
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ICPR
2008
IEEE
16 years 25 days ago
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model
In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM...
Wei Wang, Chunheng Wang, Xia Cui, Ai Wang
CIVR
2006
Springer
219views Image Analysis» more  CIVR 2006»
15 years 10 months ago
Bayesian Learning of Hierarchical Multinomial Mixture Models of Concepts for Automatic Image Annotation
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
Rui Shi, Tat-Seng Chua, Chin-Hui Lee, Sheng Gao
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 9 months ago
Multi-Instance Mixture Models
Multi-instance (MI) learning is a variant of supervised learning where labeled examples consist of bags (i.e. multi-sets) of feature vectors instead of just a single feature vecto...
James R. Foulds, Padhraic Smyth
162
Voted
VTC
2007
IEEE
109views Communications» more  VTC 2007»
16 years 19 days ago
Asymptotic BEP and SEP of MRC in Correlated Ricean Fading and Non-Gaussian Noise
— In this paper, we study the asymptotic behavior of the bit–error probability (BEP) and symbol–error probability (SEP) of coherent maximum–ratio combining (MRC) in correla...
Ali Nezampour, Amir Nasri, Robert Schober, Yao Ma
IDA
2009
Springer
16 years 28 days ago
Underdetermined Instantaneous Audio Source Separation via Local Gaussian Modeling
Underdetermined source separation is often carried out by modeling time-frequency source coefficients via a fixed sparse prior. This approach fails when the number of active sourc...
Emmanuel Vincent, Simon Arberet, Rémi Gribo...