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» On Learning Mixtures of Heavy-Tailed Distributions
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TMI
2010
182views more  TMI 2010»
13 years 7 months ago
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
Seyoung Kim, Padhraic Smyth, Hal S. Stern
INTERSPEECH
2010
13 years 3 months ago
Bayesian speaker recognition using Gaussian mixture model and laplace approximation
This paper presents a Bayesian approach for Gaussian mixture model (GMM)-based speaker identification. Some approaches evaluate the speaker score of a test speech utterance using ...
Shih-Sian Cheng, I-Fan Chen, Hsin-Min Wang
ICML
2005
IEEE
14 years 9 months ago
Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
Edward Snelson, Zoubin Ghahramani
AAAI
2000
13 years 10 months ago
Unsupervised Learning and Interactive Jazz/Blues Improvisation
We present a new domain for unsupervised learning: automatically customizing the computer to a specific melodic performer by merely listening to them improvise. We also describe B...
Belinda Thom
BMVC
2001
13 years 11 months ago
Learning Pixel-Wise Signal Energy for Understanding Semantics
Visual interpretation of events requires both an appropriate representation of change occurring in the scene and the application of semantics for differentiating between different...
Jeffrey Ng, Shaogang Gong