Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Nonparametrics for Non-exhaustive Learning   Non-exhaustive learning (NEL) is an emerging machine-learning paradigm
designed to confront the challenge of non-stationary environments characterized
by anon-exhaustive training sets lacking full information about the available
classes.Unlike traditional supervised learning that relies on fixed models, NEL
utilizes self-adjusting machine learning to better accommodate the
non-stationary nature of the real-world problem, which is at the root of many
recently discovered limitations of deep learning. Some of these hurdles led to
a surge of interest in several research areas relevant to NEL such as open set
classification or zero-shot learning. The presented study which has been
motivated by two important applications proposes a NEL algorithm built on a
highly flexible, doubly non-parametric Bayesian Gaussian mixture model that can
grow arbitrarily large in terms of the number of classes and their components.
We report several experiments that demonstrate the promising performance of the
introduced model for NEL.

23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys