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ICPR
2008
IEEE
14 years 3 months ago
Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach
This paper 1 proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian componen...
Pierrick Bruneau, Marc Gelgon, Fabien Picarougne
JMLR
2010
113views more  JMLR 2010»
13 years 3 months ago
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru M...
NAACL
2010
13 years 6 months ago
Bayesian Inference for Finite-State Transducers
We describe a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-toend data. We also investigate the problem of automat...
David Chiang, Jonathan Graehl, Kevin Knight, Adam ...
IPPS
2007
IEEE
14 years 2 months ago
Protein Secondary Structure Prediction using Bayesian Inference method on Decision fusion algorithms
Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging task, and the problem has been approached from...
Somasheker Akkaladevi, Ajay K. Katangur
ADMA
2006
Springer
121views Data Mining» more  ADMA 2006»
14 years 2 months ago
A New Polynomial Time Algorithm for Bayesian Network Structure Learning
We propose a new algorithm called SCD for learning the structure of a Bayesian network. The algorithm is a kind of constraintbased algorithm. By taking advantage of variable orderi...
Sanghack Lee, Jihoon Yang, Sungyong Park