153 search results - page 1 / 31 » Supervised feature selection via dependence estimation |
ICML
2007
IEEE
14 years 5 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 ...
JMLR
2010
12 years 11 months ago
2010
The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. ...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
FUIN
2010
12 years 11 months ago
2010
Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In t...
PKDD
2009
Springer
13 years 10 months ago
2009
Springer
Abstract. This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervisio...