467,241 research outputs found
Cops vs. Gambler
We consider a variation of cop vs.\ robber on graph in which the robber is
not restricted by the graph edges; instead, he picks a time-independent
probability distribution on and moves according to this fixed
distribution. The cop moves from vertex to adjacent vertex with the goal of
minimizing expected capture time. Players move simultaneously. We show that
when the gambler's distribution is known, the expected capture time (with best
play) on any connected -vertex graph is exactly . We also give bounds on
the (generally greater) expected capture time when the gambler's distribution
is unknown to the cop.Comment: 6 pages, 0 figure
Structural Data Recognition with Graph Model Boosting
This paper presents a novel method for structural data recognition using a
large number of graph models. In general, prevalent methods for structural data
recognition have two shortcomings: 1) Only a single model is used to capture
structural variation. 2) Naive recognition methods are used, such as the
nearest neighbor method. In this paper, we propose strengthening the
recognition performance of these models as well as their ability to capture
structural variation. The proposed method constructs a large number of graph
models and trains decision trees using the models. This paper makes two main
contributions. The first is a novel graph model that can quickly perform
calculations, which allows us to construct several models in a feasible amount
of time. The second contribution is a novel approach to structural data
recognition: graph model boosting. Comprehensive structural variations can be
captured with a large number of graph models constructed in a boosting
framework, and a sophisticated classifier can be formed by aggregating the
decision trees. Consequently, we can carry out structural data recognition with
powerful recognition capability in the face of comprehensive structural
variation. The experiments shows that the proposed method achieves impressive
results and outperforms existing methods on datasets of IAM graph database
repository.Comment: 8 page
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