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ICMCS
2009
IEEE
189views Multimedia» more  ICMCS 2009»
13 years 6 months ago
Emotion recognition from speech VIA boosted Gaussian mixture models
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, ...
Hao Tang, Stephen M. Chu, Mark Hasegawa-Johnson, T...
AI
2002
Springer
13 years 8 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
IJCAI
2007
13 years 10 months ago
Compiling Bayesian Networks Using Variable Elimination
Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of comp...
Mark Chavira, Adnan Darwiche
ECSQARU
2005
Springer
14 years 2 months ago
On the Use of Restrictions for Learning Bayesian Networks
In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a g...
Luis M. de Campos, Javier Gomez Castellano
UAI
1996
13 years 10 months ago
Context-Specific Independence in Bayesian Networks
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of th...
Craig Boutilier, Nir Friedman, Moisés Golds...