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» Solving Hierarchical Optimization Problems Using MOEAs
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151
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ICML
2001
IEEE
16 years 4 months ago
Direct Policy Search using Paired Statistical Tests
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
Malcolm J. A. Strens, Andrew W. Moore
124
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PREMI
2005
Springer
15 years 9 months ago
Learning to Segment Document Images
A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike tr...
K. S. Sesh Kumar, Anoop M. Namboodiri, C. V. Jawah...
131
Voted
ACL
2010
15 years 1 months ago
Global Learning of Focused Entailment Graphs
We propose a global algorithm for learning entailment relations between predicates. We define a graph structure over predicates that represents entailment relations as directed ed...
Jonathan Berant, Ido Dagan, Jacob Goldberger
126
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ATAL
2006
Springer
15 years 7 months ago
No-commitment branch and bound search for distributed constraint optimization
We present a new polynomial-space algorithm for solving Distributed Constraint Optimization problems (DCOP). The algorithm, called NCBB, is branch and bound search with modificati...
Anton Chechetka, Katia P. Sycara
WSC
2007
15 years 5 months ago
Parallel cross-entropy optimization
The Cross-Entropy (CE) method is a modern and effective optimization method well suited to parallel implementations. There is a vast array of problems today, some of which are hig...
Gareth E. Evans, Jonathan M. Keith, Dirk P. Kroese