148 search results - page 4 / 30 » Feature space perspectives for learning the kernel |
NIPS
2004
15 years 1 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...
103
click to vote
EMNLP
2009
14 years 9 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. ...
120
click to vote
ECAI
2010
Springer
14 years 9 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 ...
102
Voted
ICML
2003
IEEE
16 years 14 days 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
16 years 14 days 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...