A geometric perspective of large-margin training of gaussian models

Large-margin techniques have been studied intensively by the machine learning community to balance the empirical error rate on the training set and the generalization ability on the test set. However, they have been mostly developed together with generic discriminative models such as support vector machines (SVMs) and are often difficult to apply in parameter estimation problems for generative models such as Gaussians and hidden Markov models. The difficulties lie in both the formulation of the training criteria and the development of efficient optimization algorithms.