NICTA and Australia National University, Canberra, Australia.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2454-67. doi: 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.
基于似然的图形模型学习面临计算复杂性和对模型失配稳健性的挑战。本文研究了在训练时直接拟合参数以最大化预测边缘准确性度量的方法，同时考虑了模型和推理的近似。在成像问题上的实验表明，在拟合的模型本质上是近似的困难问题上，基于边缘的学习比基于似然的近似表现更好。
