Sequential Maximum Margin Classifiers for Partially Labeled Data
This addresses the challenge of real-time analysis for applications with streaming data, but it appears incremental as it builds on existing maximum margin and regularization techniques.
The authors tackled the problem of learning from data that arrives sequentially and is partially labeled by proposing a framework to update a maximum margin classifier incrementally, leveraging the Maximum Entropy Discrimination principle. They demonstrated its performance compared to non-sequential equivalents on simulated and real datasets, though no concrete numbers were provided.
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.