CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.10 - Oct.
Issue No.10 - Oct. (2013 vol.35)
J. Domke , NICTA, Australia Nat. Univ., Canberra, ACT, Australia
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.31
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.
Vectors, Entropy, Approximation algorithms, Optimization, Function approximation, Markov processes,segmentation, Graphical models, conditional random fields, machine learning, inference
J. Domke, "Learning Graphical Model Parameters with Approximate Marginal Inference", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 10, pp. 2454-2467, Oct. 2013, doi:10.1109/TPAMI.2013.31