Abstract-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.
Experimental comparison of multiple learning approaches for CRFs REF suggest that marginalization-based learning performs better than likelihood-based approximations on difficult problems where the model being fit is approximate in nature.
Learning Graphical Model Parameters with Approximate Marginal Inference
IEEE transactions on pattern analysis and machine intelligence
IEEE transactions on pattern analysis and machine intelligence
Computer Science Medicine