Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Alternate Estimation of a Classifier and the Class-Prior from Positive
  and Unlabeled Data   We consider a problem of learning a binary classifier only from positive data
and unlabeled data (PU learning) and estimating the class-prior in unlabeled
data under the case-control scenario. Most of the recent methods of PU learning
require an estimate of the class-prior probability in unlabeled data, and it is
estimated in advance with another method. However, such a two-step approach
which first estimates the class prior and then trains a classifier may not be
the optimal approach since the estimation error of the class-prior is not taken
into account when a classifier is trained. In this paper, we propose a novel
unified approach to estimating the class-prior and training a classifier
alternately. Our proposed method is simple to implement and computationally
efficient. Through experiments, we demonstrate the practical usefulness of the
proposed method.

34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics