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» Imitation Learning Using Graphical Models
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ICASSP
2011
IEEE
13 years 7 days ago
Maximum margin structure learning of Bayesian network classifiers
Recently, the margin criterion has been successfully used for parameter optimization in graphical models. We introduce maximum margin based structure learning for Bayesian network...
Franz Pernkop, Michael Wohlmay, Manfred Mücke
JETAI
1998
110views more  JETAI 1998»
13 years 8 months ago
Independency relationships and learning algorithms for singly connected networks
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
Luis M. de Campos
AAAI
2008
13 years 10 months ago
Structure Learning on Large Scale Common Sense Statistical Models of Human State
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
William Pentney, Matthai Philipose, Jeff A. Bilmes
CORR
2010
Springer
96views Education» more  CORR 2010»
13 years 8 months ago
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive threshol...
Vincent Y. F. Tan, Animashree Anandkumar, Alan S. ...
TVCG
2012
191views Hardware» more  TVCG 2012»
11 years 11 months ago
Facial Performance Transfer via Deformable Models and Parametric Correspondence
—The issue of transferring facial performance from one person’s face to another’s has been an area of interest for the movie industry and the computer graphics community for ...
Akshay Asthana, Miles de la Hunty, Abhinav Dhall, ...