Semi-supervised Learning for Improved Expression of Uncertainty in Discriminative Classifiers.

Seeking classifier models that are not overconfident and that better represent the inherent uncertainty over a set of choices, we extend an objective for semi-supervised learning for neural networks to two models from the ratio semi-definite classifier (RSC) family. We show that the RSC family of classifiers produces smoother transitions between classes on a vowel classification task, and that the semi-supervised framework provides further benefits for smooth transitions. Finally, our testing methodology presents a novel way to evaluate the smoothness of classifier transitions (interpolating between vowels) by using samples from classes unseen during training time.