This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Learning Graphical Model Parameters with Approximate Marginal Inference
Oct. 2013 (vol. 35 no. 10)
pp. 2454-2467
J. Domke, NICTA, Australia Nat. Univ., Canberra, ACT, Australia
Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals, taking into account both model and inference approximations at training time. Experiments on imaging problems suggest marginalization-based learning performs better than likelihood-based approximations on difficult problems where the model being fit is approximate in nature.
Index Terms:
Vectors,Entropy,Approximation algorithms,Optimization,Function approximation,Markov processes,segmentation,Graphical models,conditional random fields,machine learning,inference
Citation:
J. Domke, "Learning Graphical Model Parameters with Approximate Marginal Inference," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, pp. 2454-2467, Oct. 2013, doi:10.1109/TPAMI.2013.31
Usage of this product signifies your acceptance of the Terms of Use.