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» The C1C2: A framework for simultaneous model selection and a...
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PAMI
2006
215views more  PAMI 2006»
13 years 5 months ago
Bayesian Feature and Model Selection for Gaussian Mixture Models
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
EMNETS
2007
13 years 9 months ago
An HMM framework for optimal sensor selection with applications to BSN sensor glove design
Laparoscopic surgical training is a challenging task due to the complexity of instrument control and demand on manual dexterity and hand-eye coordination. Currently, training and ...
Rachel C. King, Louis Atallah, Ara Darzi, Guang-Zh...
ESANN
2008
13 years 6 months ago
A method for robust variable selection with significance assessment
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...
BMCBI
2008
171views more  BMCBI 2008»
13 years 5 months ago
A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more
Background: With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables ...
Harri T. Kiiveri
AMC
2007
92views more  AMC 2007»
13 years 5 months ago
An integrated framework for continuous assessment and improvement of manufacturing systems
This paper presents an integrated framework for assessment and ranking of manufacturing systems based on management and organizational performance indicators. The integrated appro...
Ali Azadeh, S. F. Ghaderi, Y. Partovi Miran, V. Eb...