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» Random subspace method for multivariate feature selection
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DATE
2009
IEEE
92views Hardware» more  DATE 2009»
14 years 3 months ago
Using randomization to cope with circuit uncertainty
—Future computing systems will feature many cores that run fast, but might show more faults compared to existing CMOS technologies. New software methodologies must be adopted to ...
Hamid Safizadeh, Mohammad Tahghighi, Ehsan K. Arde...
BMCBI
2007
217views more  BMCBI 2007»
13 years 8 months ago
On consensus biomarker selection
Background: Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biom...
Janusz Dutkowski, Anna Gambin
CVPR
2012
IEEE
11 years 11 months ago
On SIFTs and their scales
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: mat...
Tal Hassner, Viki Mayzels, Lihi Zelnik-Manor
NN
2000
Springer
177views Neural Networks» more  NN 2000»
13 years 8 months ago
Independent component analysis: algorithms and applications
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons...
Aapo Hyvärinen, Erkki Oja
CVPR
2007
IEEE
14 years 10 months ago
Sparse Kernels for Bayes Optimal Discriminant Analysis
Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research ? especially in the C class classificat...
Aleix M. Martínez, Onur C. Hamsici