DAWAK
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. This paper addresses a task of variable selection which consists in choosing a subset of variables that is sufficient to predict the target label well. Here instead of tr...
ICML
2010
IEEE
14 years 17 days ago
2010
IEEE
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data ...
AAAI
1997
14 years 26 days ago
1997
One of the important components of a local search strategy for satisfiability testing is the variable selection heuristic, which determines the next variable to be flipped. In a...
ESANN
2006
14 years 27 days ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
COLT
2008
Springer
14 years 1 months ago
2008
Springer
Coordinate gradient learning is motivated by the problem of variable selection and determining variable covariation. In this paper we propose a novel unifying framework for coordi...
AAAI
2007
14 years 1 months ago
2007
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 ...
IDA
2003
Springer
14 years 4 months ago
2003
Springer
This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform var...
GFKL
2005
Springer
14 years 5 months ago
2005
Springer
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...
AIME
2007
Springer
14 years 5 months ago
2007
Springer
This paper discusses variable selection for medical decision making; in particular decisions regarding when to provide treatment and which treatment to provide. Current variable se...
IJCNN
2007
IEEE
14 years 5 months ago
2007
IEEE
– Constructive algorithms are effective methods for designing Artificial Neural Networks (ANN) with good accuracy and generalization capability, yet with parsimonious network str...