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» Optimization Problems in Congestion Control
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ICML
2006
IEEE
16 years 5 months ago
A statistical approach to rule learning
We present a new, statistical approach to rule learning. Doing so, we address two of the problems inherent in traditional rule learning: The computational hardness of finding rule...
Stefan Kramer, Ulrich Rückert
IAT
2009
IEEE
15 years 11 months ago
Towards Zero-Delay Recovery of Agents in Production Automation Systems
Multi-agent systems (MAS) is an accepted paradigm in safety-critical systems, like the production automation. Agents control the underlying machinery they are representing and int...
eva Kühn, Richard Mordinyi, Mario Lang, Adnan...
129
Voted
GECCO
2006
Springer
206views Optimization» more  GECCO 2006»
15 years 7 months ago
Adaptive discretization for probabilistic model building genetic algorithms
This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization pro...
Chao-Hong Chen, Wei-Nan Liu, Ying-Ping Chen
138
Voted
GECCO
2006
Springer
138views Optimization» more  GECCO 2006»
15 years 7 months ago
Does overfitting affect performance in estimation of distribution algorithms
Estimation of Distribution Algorithms (EDAs) are a class of evolutionary algorithms that use machine learning techniques to solve optimization problems. Machine learning is used t...
Hao Wu, Jonathan L. Shapiro
CORR
2008
Springer
113views Education» more  CORR 2008»
15 years 4 months ago
Robustness, Risk, and Regularization in Support Vector Machines
We consider two new formulations for classification problems in the spirit of support vector machines based on robust optimization. Our formulations are designed to build in prote...
Huan Xu, Shie Mannor, Constantine Caramanis