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GECCO
2005
Springer
119views Optimization» more  GECCO 2005»
14 years 1 months ago
Learning, anticipation and time-deception in evolutionary online dynamic optimization
In this paper we focus on an important source of problem– difficulty in (online) dynamic optimization problems that has so far received significantly less attention than the tr...
Peter A. N. Bosman
ICMLA
2007
13 years 9 months ago
Learning bayesian networks consistent with the optimal branching
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branch...
Alexandra M. Carvalho, Arlindo L. Oliveira
TWC
2008
120views more  TWC 2008»
13 years 7 months ago
Cooperation Enforcement and Learning for Optimizing Packet Forwarding in Autonomous Wireless Networks
In wireless ad hoc networks, autonomous nodes are reluctant to forward others' packets because of the nodes' limited energy. However, such selfishness and noncooperation ...
Charles Pandana, Zhu Han, K. J. Ray Liu
CORR
2010
Springer
146views Education» more  CORR 2010»
13 years 7 months ago
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...
Daniel Golovin, Andreas Krause
CVPR
2009
IEEE
15 years 2 months ago
Learning Optimized MAP Estimates in Continuously-Valued MRF Models
We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing t...
Kegan G. G. Samuel, Marshall F. Tappen