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ICML
2006
IEEE
15 years 11 months ago
Efficient MAP approximation for dense energy functions
We present an efficient method for maximizing energy functions with first and second order potentials, suitable for MAP labeling estimation problems that arise in undirected graph...
Marius Leordeanu, Martial Hebert
ICRA
2008
IEEE
119views Robotics» more  ICRA 2008»
15 years 4 months ago
Maximum likelihood estimation of sensor and action model functions on a mobile robot
— In order for a mobile robot to accurately interpret its sensations and predict the effects of its actions, it must have accurate models of its sensors and actuators. These mode...
Daniel Stronger, Peter Stone
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 3 months ago
A pareto archive evolutionary strategy based radial basis function neural network training algorithm for failure rate prediction
This paper outlines a radial basis function neural network approach to predict the failures in overhead distribution lines of power delivery systems. The RBF networks are trained ...
Grant Cochenour, Jerad Simon, Sanjoy Das, Anil Pah...
ITICSE
1997
ACM
15 years 2 months ago
A genetic algorithms tutorial tool for numerical function optimisation
The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation pro...
Edmund K. Burke, D. B. Varley
CORR
2010
Springer
70views Education» more  CORR 2010»
14 years 10 months ago
Structured sparsity-inducing norms through submodular functions
Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports. This combinatorial ...
Francis Bach