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ICML
2004
IEEE
15 years 10 months ago
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
Yongdai Kim, Jinseog Kim
JMLR
2002
138views more  JMLR 2002»
15 years 4 months ago
Text Chunking based on a Generalization of Winnow
This paper describes a text chunking system based on a generalization of the Winnow algorithm. We propose a general statistical model for text chunking which we then convert into ...
Tong Zhang, Fred Damerau, David Johnson
RC
2007
83views more  RC 2007»
15 years 3 months ago
Computing the Pessimism of Inclusion Functions
Abstract. “Computing the pessimism” means bounding the overestimation produced by an inclusion function. There are two important distinctions with classical error analysis. Fir...
Gilles Chabert, Luc Jaulin
NA
2010
69views more  NA 2010»
15 years 2 months ago
Partial spectral projected gradient method with active-set strategy for linearly constrained optimization
A method for linearly constrained optimization which modifies and generalizes recent box-constraint optimization algorithms is introduced. The new algorithm is based on a relaxed...
Marina Andretta, Ernesto G. Birgin, José Ma...
SIAMSC
2010
161views more  SIAMSC 2010»
15 years 2 months ago
Surface Reconstruction and Image Enhancement via L1-Minimization
A surface reconstruction technique based on minimization of the total variation of the gradient is introduced. Convergence of the method is established, and an interior-point algor...
Veselin Dobrev, Jean-Luc Guermond, Bojan Popov