Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A generalized linear joint trained framework for semi-supervised
  learning of sparse features   The elastic-net is among the most widely used types of regularization
algorithms, commonly associated with the problem of supervised generalized
linear model estimation via penalized maximum likelihood. Its nice properties
originate from a combination of $\ell_1$ and $\ell_2$ norms, which endow this
method with the ability to select variables taking into account the
correlations between them. In the last few years, semi-supervised approaches,
that use both labeled and unlabeled data, have become an important component in
the statistical research. Despite this interest, however, few researches have
investigated semi-supervised elastic-net extensions. This paper introduces a
novel solution for semi-supervised learning of sparse features in the context
of generalized linear model estimation: the generalized semi-supervised
elastic-net (s2net), which extends the supervised elastic-net method, with a
general mathematical formulation that covers, but is not limited to, both
regression and classification problems. We develop a flexible and fast
implementation for s2net in R, and its advantages are illustrated using both
real and synthetic data sets.

5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph