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CVIU
2011
14 years 3 months ago
Graph attribute embedding via Riemannian submersion learning
In this paper, we tackle the problem of embedding a set of relational structures into a metric space for purposes of matching and categorisation. To this end, we view the problem ...
Haifeng Zhao, Antonio Robles-Kelly, Jun Zhou, Jian...
117
Voted
ICCS
2005
Springer
15 years 5 months ago
Analyzing Conflicts with Concept-Based Learning
A machine learning technique for handling scenarios of interaction between conflicting agents is suggested. Scenarios are represented by directed graphs with labeled vertices (for ...
Boris Galitsky, Sergei O. Kuznetsov, Mikhail V. Sa...
99
Voted
CVPR
2007
IEEE
16 years 1 months ago
Image Classification with Segmentation Graph Kernels
We propose a family of kernels between images, defined as kernels between their respective segmentation graphs. The kernels are based on soft matching of subtree-patterns of the r...
Francis Bach, Zaïd Harchaoui
ICALP
2004
Springer
15 years 5 months ago
Learning a Hidden Subgraph
We consider the problem of learning a labeled graph from a given family of graphs on n vertices in a model where the only allowed operation is to query whether a set of vertices i...
Noga Alon, Vera Asodi
ICML
2007
IEEE
16 years 13 days ago
Scalable modeling of real graphs using Kronecker multiplication
Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum,...
Jure Leskovec, Christos Faloutsos