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ICPR
2008
IEEE
15 years 10 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»
14 years 10 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
15 years 1 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
15 years 10 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»
15 years 9 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