Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Feature Selection via Dependence Estimation   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 key idea is that good features should
maximise such dependence. Feature selection for various supervised learning
problems (including classification and regression) is unified under this
framework, and the solutions can be approximated using a backward-elimination
algorithm. We demonstrate the usefulness of our method on both artificial and
real world datasets.

6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom