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» Kernels and Regularization on Graphs
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94
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MFCS
2007
Springer
15 years 6 months ago
Uncover Low Degree Vertices and Minimise the Mess: Independent Sets in Random Regular Graphs
Abstract. We present algorithmic lower bounds on the size of the largest independent sets of vertices in a random d-regular graph. Our bounds hold with probability approaching one ...
William Duckworth, Michele Zito
ICPR
2008
IEEE
16 years 1 months ago
Image retrieval with graph kernel on regions
In the framework of the interactive search in image databases, we are interested in similarity measures able to learn during the search and usable in real-time. Images are represe...
Justine Lebrun, Sylvie Philipp-Foliguet, Philippe ...
ISAAC
2007
Springer
111views Algorithms» more  ISAAC 2007»
15 years 6 months ago
Problem Kernels for NP-Complete Edge Deletion Problems: Split and Related Graphs
In an edge deletion problem one is asked to delete at most k edges from a given graph such that the resulting graph satisfies a certain property. In this work, we study four NP-co...
Jiong Guo
96
Voted
RSA
2010
108views more  RSA 2010»
14 years 11 months ago
Resolvent of large random graphs
We analyze the convergence of the spectrum of large random graphs to the spectrum of a limit infinite graph. We apply these results to graphs converging locally to trees and deri...
Charles Bordenave, Marc Lelarge
ICML
2004
IEEE
16 years 1 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu