Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effective and efficient structure learning with pruning and model
  averaging strategies   Learning the structure of a Bayesian Network (BN) with score-based solutions
involves exploring the search space of possible graphs and moving towards the
graph that maximises a given objective function. Some algorithms offer exact
solutions that guarantee to return the graph with the highest objective score,
while others offer approximate solutions in exchange for reduced computational
complexity. This paper describes an approximate BN structure learning
algorithm, which we call Model Averaging Hill-Climbing (MAHC), that combines
two novel strategies with hill-climbing search. The algorithm starts by pruning
the search space of graphs, where the pruning strategy can be viewed as an
aggressive version of the pruning strategies that are typically applied to
combinatorial optimisation structure learning problems. It then performs model
averaging in the hill-climbing search process and moves to the neighbouring
graph that maximises the objective function, on average, for that neighbouring
graph and over all its valid neighbouring graphs. Comparisons with other
algorithms spanning different classes of learning suggest that the combination
of aggressive pruning with model averaging is both effective and efficient,
particularly in the presence of data noise.

28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess