Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Ensemble Classification with Sequential and Networked Data   Ensemble learning, the machine learning paradigm where multiple algorithms
are combined, has exhibited promising perfomance in a variety of tasks. The
present work focuses on unsupervised ensemble classification. The term
unsupervised refers to the ensemble combiner who has no knowledge of the
ground-truth labels that each classifier has been trained on. While most prior
works on unsupervised ensemble classification are designed for independent and
identically distributed (i.i.d.) data, the present work introduces an
unsupervised scheme for learning from ensembles of classifiers in the presence
of data dependencies. Two types of data dependencies are considered: sequential
data and networked data whose dependencies are captured by a graph. Moment
matching and Expectation Maximization algorithms are developed for the
aforementioned cases, and their performance is evaluated on synthetic and real
datasets.

2
The categories of this paper are: math
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas