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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 5 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
ICDE
2008
IEEE
160views Database» more  ICDE 2008»
14 years 9 months ago
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
We study the problem of computing query results with confidence values in ULDBs: relational databases with uncertainty and lineage. ULDBs, which subsume probabilistic databases, o...
Anish Das Sarma, Martin Theobald, Jennifer Widom
DATE
2008
IEEE
101views Hardware» more  DATE 2008»
14 years 2 months ago
Resilient Dynamic Power Management under Uncertainty
With the increasing levels of variability and randomness in the characteristics and behavior of manufactured nanoscale structures and devices, achieving performance optimization u...
Hwisung Jung, Massoud Pedram
IROS
2007
IEEE
101views Robotics» more  IROS 2007»
14 years 2 months ago
Optimizing image and camera trajectories in robot vision control using on-line boosting
— In this paper, we present a novel boosted robot vision control algorithm. The method utilizes on-line boosting to produce a strong vision-based robot control starting from two ...
A. H. Abdul Hafez, Enric Cervera, C. V. Jawahar
AUTOMATICA
2008
82views more  AUTOMATICA 2008»
13 years 8 months ago
Iterative minimization of H2 control performance criteria
Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance crit...
Alexandre S. Bazanella, Michel Gevers, Ljubisa Mis...