Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hidden Variables Approach to Multilabel Logistic Regression   Multilabel classification is an important problem in a wide range of domains
such as text categorization and music annotation. In this paper, we present a
probabilistic model, Multilabel Logistic Regression with Hidden variables
(MLRH), which extends the standard logistic regression by introducing hidden
variables. Hidden variables make it possible to go beyond the conventional
multiclass logistic regression by relaxing the one-hot-encoding constraint. We
define a new joint distribution of labels and hidden variables which enables us
to obtain one classifier for multilabel classification. Our experimental
studies on a set of benchmark datasets demonstrate that the probabilistic model
can achieve competitive performance compared with other multilabel learning
algorithms.

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The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio