167 search results - page 1 / 34 » Supervised Feature Extraction Using Hilbert-Schmidt Norms |
IDEAL
2009
Springer
13 years 11 months ago
2009
Springer
We propose a novel, supervised feature extraction procedure, based on an unbiased estimator of the Hilbert-Schmidt independence criterion (HSIC). The proposed procedure can be dire...
ICML
2007
IEEE
14 years 6 months ago
2007
IEEE
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
KDD
2008
ACM
14 years 5 months ago
2008
ACM
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kern...
COLING
2008
13 years 6 months ago
2008
It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are cat...
ICANN
2001
Springer
13 years 9 months ago
2001
Springer
In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving ...