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» Iterative Methods in Combinatorial Optimization
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GECCO
2004
Springer
131views Optimization» more  GECCO 2004»
15 years 5 months ago
PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality Constraints
Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models of good solutions to guide their search. When applied to co...
Jörn Grahl, Franz Rothlauf
AI
2002
Springer
14 years 11 months ago
Multiagent learning using a variable learning rate
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Michael H. Bowling, Manuela M. Veloso
JMLR
2010
121views more  JMLR 2010»
14 years 6 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
AUSSOIS
2001
Springer
15 years 4 months ago
An Augment-and-Branch-and-Cut Framework for Mixed 0-1 Programming
In recent years the branch-and-cut method, a synthesis of the classical branch-and-bound and cutting plane methods, has proven to be a highly successful approach to solving large-s...
Adam N. Letchford, Andrea Lodi
TWC
2010
14 years 6 months ago
Distributed consensus-based demodulation: algorithms and error analysis
This paper deals with distributed demodulation of space-time transmissions of a common message from a multiantenna access point (AP) to a wireless sensor network. Based on local me...
Hao Zhu, Alfonso Cano, Georgios B. Giannakis