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» Modelling Profiles with a Mixture of Gaussians
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ESANN
2007
13 years 9 months ago
Markovian blind separation of non-stationary temporally correlated sources
In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
Rima Guidara, Shahram Hosseini, Yannick Deville
ESANN
2008
13 years 9 months ago
Phase transitions in Vector Quantization
Abstract. We study Winner-Takes-All and rank based Vector Quantization along the lines of the statistical physics of off-line learning. Typical behavior of the system is obtained w...
Aree Witoelar, Anarta Ghosh, Michael Biehl
NIPS
2003
13 years 9 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
ICASSP
2010
IEEE
13 years 8 months ago
Non-parallel training for many-to-many eigenvoice conversion
This paper presents a novel training method of an eigenvoice Gaussian mixture model (EV-GMM) effectively using non-parallel data sets for many-to-many eigenvoice conversion, which...
Yamato Ohtani, Tomoki Toda, Hiroshi Saruwatari, Ki...
IJON
2006
99views more  IJON 2006»
13 years 8 months ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer