We compare the practical performance of several recently proposed algorithms for active learning in the online setting. We consider two algorithms (and their combined variants) that are strongly online, in that they do ...

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate ...

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a class of universal learning algorithms involving ...