848 search results - page 1 / 170 » Feature selection in a kernel space |
ICML
2007
IEEE
14 years 5 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 1 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 6 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 4 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 5 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...