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» Approximate algorithms for neural-Bayesian approaches
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ICRA
2008
IEEE
150views Robotics» more  ICRA 2008»
15 years 4 months ago
A Bayesian approach to empirical local linearization for robotics
— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics system...
Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, St...
GECCO
2008
Springer
129views Optimization» more  GECCO 2008»
14 years 11 months ago
Fitness calculation approach for the switch-case construct in evolutionary testing
A well-designed fitness function is essential to the effectiveness and efficiency of evolutionary testing. Fitness function design has been researched extensively. For fitness ...
Yan Wang, Zhiwen Bai, Miao Zhang, Wen Du, Ying Qin...
AAAI
2006
14 years 11 months ago
Point-based Dynamic Programming for DEC-POMDPs
We introduce point-based dynamic programming (DP) for decentralized partially observable Markov decision processes (DEC-POMDPs), a new discrete DP algorithm for planning strategie...
Daniel Szer, François Charpillet
ECML
2005
Springer
15 years 3 months ago
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin ...
EMO
2005
Springer
194views Optimization» more  EMO 2005»
15 years 3 months ago
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion
Abstract. The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea t...
Michael Emmerich, Nicola Beume, Boris Naujoks