  In many real-world applications, data is not collected as one batch, but
sequentially over time, and often it is not possible or desirable to wait until
the data is completely gathered before analyzing it. Thus, we propose a
framework to sequentially update a maximum margin classifier by taking
advantage of the Maximum Entropy Discrimination principle. Our maximum margin
classifier allows for a kernel representation to represent large numbers of
features and can also be regularized with respect to a smooth sub-manifold,
allowing it to incorporate unlabeled observations. We compare the performance
of our classifier to its non-sequential equivalents in both simulated and real
datasets.
