Practical Online Active Learning for Classification
Claire Monteleoni and Matti Kääriäinen
In: IEEE Conference on Computer Vision and Pattern Recognition, Online Learning and Classification Workshop, 2007, 18-23 June 2007, minneapolis, missisippi.
We compare the practical performance of several recently proposed algorithms for active learning in the online classification setting. We consider two active learning algorithms (and their combined variants) that are strongly online, in that they access the data sequentially and do not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We motivate an optical character recognition (OCR) application that we argue to be appropriately served by online active learning. We compare the practical efficacy, for this application, of the algorithm variants, and show significant reductions in label-complexity over random sampling.