ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater und...
DRR
2009
13 years 10 months ago
2009
When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with...
ISSRE
2010
IEEE
13 years 11 months ago
2010
IEEE
—Database-centric applications (DCAs) are common in enterprise computing, and they use nontrivial databases. Testing of DCAs is increasingly outsourced to test centers in order t...
BMCBI
2005
14 years 10 days ago
2005
Background: Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative e...
ISI
2006
Springer
14 years 12 days ago
2006
Springer
This work investigates the accuracy and efficiency tradeoffs between centralized and collective (distributed) algorithms for (i) sampling, and (ii) n-way data analysis techniques i...
BMCBI
2006
14 years 15 days ago
2006
Background: We present a general approach to perform association analyses in pedigrees of arbitrary size and structure, which also allows for a mixture of pedigree members and ind...
BMCBI
2006
14 years 15 days ago
2006
Background: Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-proce...
BMCBI
2008
14 years 16 days ago
2008
Background: With the completion of the HapMap project, a variety of computational algorithms and tools have been proposed for haplotype inference, tag SNP selection and genome-wid...
BMVC
1998
14 years 1 months ago
1998
The classification of sets of mixed pixels can be accomplished by making use of the relationship of higher order moments of the distributions of the pure and mixed classes. As a c...
ESANN
2004
14 years 1 months ago
2004
Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D m...