120 search results - page 8 / 24 » Convex Principal Feature Selection |
NIPS
2007
13 years 9 months ago
2007
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hiera...
ESANN
2006
13 years 9 months 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 ...
PKDD
2009
Springer
14 years 2 months ago
2009
Springer
The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fi...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance...
CORR
2010
Springer
13 years 5 months ago
2010
Springer
In this paper we describe a procedure to reduce the size of the input feature vector. A complex pattern recognition problem like face recognition involves huge dimension of input ...