Online Active Learning in Practice

Abstract

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 not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We perform an empirical evaluation on optical character recognition (OCR) data, an application that we argue to be appropriately served by online active learning. We compare the performance between the algorithm variants and show significant reductions in label-complexity over random sampling.