148 search results - page 4 / 30 » Feature space perspectives for learning the kernel |
NIPS
2004
13 years 9 months ago
2004
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkabl...
EMNLP
2009
13 years 5 months ago
2009
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
ECAI
2010
Springer
13 years 5 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 ...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
In this paper, we address the problem of finding the pre-image of a feature vector in the feature space induced by a kernel. This is of central importance in some kernel applicatio...
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...