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» A Regularization Approach to Nonlinear Variable Selection
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ESANN
2008
15 years 16 days 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...
110
Voted
ICDM
2007
IEEE
129views Data Mining» more  ICDM 2007»
15 years 26 days ago
Feature Selection for Nonlinear Kernel Support Vector Machines
An easily implementable mixed-integer algorithm is proposed that generates a nonlinear kernel support vector machine (SVM) classifier with reduced input space features. A single ...
Olvi L. Mangasarian, Gang Kou
86
Voted
CORR
2007
Springer
99views Education» more  CORR 2007»
14 years 11 months ago
Fast Selection of Spectral Variables with B-Spline Compression
The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables ...
Fabrice Rossi, Damien François, Vincent Wer...
ICML
2009
IEEE
15 years 12 months ago
Optimized expected information gain for nonlinear dynamical systems
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-d...
Alberto Giovanni Busetto, Cheng Soon Ong, Joachim ...
98
Voted
AAAI
2007
15 years 1 months ago
Learning Graphical Model Structure Using L1-Regularization Paths
Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
Mark W. Schmidt, Alexandru Niculescu-Mizil, Kevin ...