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ICML
2009
IEEE
14 years 9 months ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 4 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
ICVGIP
2004
13 years 10 months ago
Multi-Cue Exemplar-Based Nonparametric Model for Gesture Recognition
This paper presents an approach for a multi-cue, viewbased recognition of gestures. We describe an exemplarbased technique that combines two different forms of exemplars - shape e...
Vinay D. Shet, V. Shiv Naga Prasad, Ahmed M. Elgam...
PVM
2009
Springer
14 years 3 months ago
Optimizing MPI Runtime Parameter Settings by Using Machine Learning
Abstract. Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for ...
Simone Pellegrini, Jie Wang, Thomas Fahringer, Han...
EMO
2009
Springer
159views Optimization» more  EMO 2009»
14 years 3 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel