Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning...
Dynamic visual category learning calls for efficient adaptation as new training images become available or new categories are defined, existing training images or categories becom...
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approximate by an ellipsoid the classi...