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» On Partitional Labelings of Graphs
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CVPR
2010
IEEE
14 years 5 months ago
Efficient Piecewise Learning for Conditional Random Fields
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization probl...
Karteek Alahari, Phil Torr
CASES
2007
ACM
14 years 1 months ago
An efficient framework for dynamic reconfiguration of instruction-set customization
We present an efficient framework for dynamic reconfiguration of application-specific custom instructions. A key component of this framework is an iterative algorithm for temporal...
Huynh Phung Huynh, Joon Edward Sim, Tulika Mitra
COLT
2004
Springer
14 years 3 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
DISOPT
2010
129views more  DISOPT 2010»
13 years 9 months ago
Labeled Traveling Salesman Problems: Complexity and approximation
We consider labeled Traveling Salesman Problems, defined upon a complete graph of n vertices with colored edges. The objective is to find a tour of maximum or minimum number of co...
Basile Couëtoux, Laurent Gourvès, J&ea...
FLAIRS
2007
13 years 12 months ago
Inference of Edge Replacement Graph Grammars
We describe an algorithm and experiments for inference of edge replacement graph grammars. This method generates candidate recursive graph grammar productions based on isomorphic ...
Jacek P. Kukluk, Lawrence B. Holder, Diane J. Cook