Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383437 | 2007 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-8 |
Keywords | Field | DocType |
computer science,optical character recognition,machine learning,random sampling,sampling methods,random processes,uncertainty,image classification,learning artificial intelligence,active learning | Semi-supervised learning,Active learning (machine learning),Computer science,Feature (machine learning),Artificial intelligence,Contextual image classification,Computer vision,Online machine learning,Active learning,Intelligent character recognition,Pattern recognition,Optical character recognition,Machine learning | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 |
Citations | PageRank | References |
17 | 0.86 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claire Monteleoni | 1 | 327 | 24.15 |
| Matti Kääriäinen | 2 | 146 | 10.57 |