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» Matching Algorithms Are Fast in Sparse Random Graphs
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JCP
2008
171views more  JCP 2008»
13 years 7 months ago
Mining Frequent Subgraph by Incidence Matrix Normalization
Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edg...
Jia Wu, Ling Chen
ICML
2008
IEEE
14 years 8 months ago
Fast estimation of first-order clause coverage through randomization and maximum likelihood
In inductive logic programming, subsumption is a widely used coverage test. Unfortunately, testing -subsumption is NP-complete, which represents a crucial efficiency bottleneck fo...
Filip Zelezný, Ondrej Kuzelka
CVPR
2004
IEEE
14 years 10 months ago
Segment-Based Stereo Matching Using Graph Cuts
In this paper, we present a new segment-based stereo matching algorithm using graph cuts. In our approach, the reference image is divided into non-overlapping homogeneous segments...
Li Hong, George Chen
APPROX
2004
Springer
105views Algorithms» more  APPROX 2004»
14 years 1 months ago
Counting Connected Graphs and Hypergraphs via the Probabilistic Method
While it is exponentially unlikely that a sparse random graph or hypergraph is connected, with probability 1 − o(1) such a graph has a “giant component” that, given its numbe...
Amin Coja-Oghlan, Cristopher Moore, Vishal Sanwala...
ACCV
2009
Springer
14 years 2 months ago
A Probabilistic Model for Correspondence Problems Using Random Walks with Restart
Abstract. In this paper, we propose an efficient method for finding consistent correspondences between two sets of features. Our matching algorithm augments the discriminative pow...
Tae Hoon Kim, Kyoung Mu Lee, Sang Uk Lee