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» Simple Randomized Algorithms for Closest Pair Problems
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ICIP
2005
IEEE
15 years 11 months ago
Off-line multiple object tracking using candidate selection and the Viterbi algorithm
This paper presents a probabilistic framework for off-line multiple object tracking. At each timestep, a small set of deterministic candidates is generated which is guaranteed to ...
Anil C. Kokaram, François Pitié, Roz...
ICML
2004
IEEE
15 years 10 months ago
Semi-supervised learning using randomized mincuts
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...
ICML
2007
IEEE
15 years 10 months ago
Solving multiclass support vector machines with LaRank
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optim...
Antoine Bordes, Jason Weston, Léon Bottou, ...
ICALP
2009
Springer
15 years 10 months ago
Correlation Clustering Revisited: The "True" Cost of Error Minimization Problems
Correlation Clustering was defined by Bansal, Blum, and Chawla as the problem of clustering a set of elements based on a possibly inconsistent binary similarity function between e...
Nir Ailon, Edo Liberty
GECCO
2010
Springer
207views Optimization» more  GECCO 2010»
15 years 2 months ago
Generalized crowding for genetic algorithms
Crowding is a technique used in genetic algorithms to preserve diversity in the population and to prevent premature convergence to local optima. It consists of pairing each offsp...
Severino F. Galán, Ole J. Mengshoel