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CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
CORR
2000
Springer
99views Education» more  CORR 2000»
14 years 9 months ago
Adaptive simulated annealing (ASA): Lessons learned
Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using othe...
Lester Ingber
CAI
2007
Springer
15 years 3 months ago
Learning Deterministically Recognizable Tree Series - Revisited
Abstract. We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The lear...
Andreas Maletti
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 1 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
ECCV
2004
Springer
15 years 11 months ago
Interactive Image Segmentation Using an Adaptive GMMRF Model
The problem of interactive foreground/background segmentation in still images is of great practical importance in image editing. The state of the art in interactive segmentation is...
Andrew Blake, Carsten Rother, M. Brown, Patrick P&...