Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency

by Georg Krempl, Daniel Kottke Mining data with minimal annotation costs requires efficient active approaches, that ideally select the optimal candidate for labelling under a user-specified classification performance measure. Common generic approaches, that are usable with any classifier and any performance measure, are either slow like error reduction, or heuristics like uncertainty sampling. In contrast, […]

Read More