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JMLR
2010
202views more  JMLR 2010»
13 years 2 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
ICANN
2007
Springer
14 years 1 months ago
Unbiased SVM Density Estimation with Application to Graphical Pattern Recognition
Abstract. Classification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different,...
Edmondo Trentin, Ernesto Di Iorio
ARTMED
2004
118views more  ARTMED 2004»
13 years 7 months ago
Bayesian fluorescence in situ hybridisation signal classification
Previous research has indicated the significance of accurate classification of fluorescence in situ hybridisation (FISH) signals for the detection of genetic abnormalities. Based ...
Boaz Lerner
CORR
2010
Springer
168views Education» more  CORR 2010»
13 years 6 months ago
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by...
Ricardo Silva
PR
2010
129views more  PR 2010»
13 years 6 months ago
Parsimonious reduction of Gaussian mixture models with a variational-Bayes approach
Aggregating statistical representations of classes is an important task for current trends in scaling up learning and recognition, or for addressing them in distributed infrastruc...
Pierrick Bruneau, Marc Gelgon, Fabien Picarougne