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» Function Optimization with Coevolutionary Algorithms
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ICCV
2009
IEEE
16 years 10 months ago
The Swap and Expansion Moves Revisited and Fused
Many solutions to computer vision and image processing problems involve the minimization of multi-label energy functions with up to K variables in each term. In the minimization pr...
Ido Leichter
GECCO
2007
Springer
164views Optimization» more  GECCO 2007»
16 years 26 days ago
Learning building block structure from crossover failure
In the classical binary genetic algorithm, although crossover within a building block (BB) does not always cause a decrease in fitness, any decrease in fitness results from the ...
Zhenhua Li, Erik D. Goodman
PAMI
2011
15 years 1 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
CVPR
2008
IEEE
16 years 8 months ago
Graph-shifts: Natural image labeling by dynamic hierarchical computing
In this paper, we present a new approach for image labeling based on the recently introduced graph-shifts algorithm. Graph-shifts is an energy minimization algorithm that does lab...
Jason J. Corso, Alan L. Yuille, Zhuowen Tu
CDC
2009
IEEE
111views Control Systems» more  CDC 2009»
15 years 11 months ago
On fusion of information from multiple sensors in the presence of analog erasure links
— Consider multiple sensors that transmit data over analog erasure links to an estimation center. The sensors have access to distinct entries of the output vector of a linear and...
Vijay Gupta, Nuno C. Martins