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GFKL
2005
Springer
105views Data Mining» more  GFKL 2005»
13 years 10 months ago
Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse
In classification, with an increasing number of variables, the required number of observations grows drastically. In this paper we present an approach to put into effect the maxi...
Gero Szepannek, Claus Weihs
BMCBI
2008
171views more  BMCBI 2008»
13 years 4 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
MICCAI
2010
Springer
13 years 3 months ago
Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding ...
Vincent Michel, Evelyn Eger, Christine Keribin, Be...
BMCBI
2007
158views more  BMCBI 2007»
13 years 4 months ago
Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries
Background: The joint analysis of several categorical variables is a common task in many areas of biology, and is becoming central to systems biology investigations whose goal is ...
Corinne Dahinden, Giovanni Parmigiani, Mark C. Eme...
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...