848 search results - page 1 / 170 » Feature selection in a kernel space |
ICML
2007
IEEE
14 years 10 months ago
2007
IEEE
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult ...
ECAI
2010
Springer
13 years 6 months ago
2010
Springer
The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket ...
NIPS
2008
13 years 10 months ago
2008
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
JMLR
2008
13 years 9 months ago
2008
We show that the relevant information of a supervised learning problem is contained up to negligible error in a finite number of leading kernel PCA components if the kernel matche...
ICML
2006
IEEE
14 years 10 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...