Name
Abstract | ||
|---|---|---|
This paper presents a practical method for pool-based active learning that is robust to annotation noise. Our work is inspired by recent approaches to active learning in two different noise-free settings: importance-weighted methods for streams and unbiased pool-based techniques. In our proposed method, we employ an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. We demonstrate, using several standard datasets, that the proposed approach, which employs label prediction in combination with importance-weighting, significantly improves active learning in the presence of annotation noise. Moreover, the ease with which the proposed method can be implemented should make it widely applicable to a broad range of real-world applications. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | importance-weighted label prediction,annotation noise,learning (artificial intelligence),pattern classification,pool-based active learning,unlabeled training data,real-world applications,unbiased pool-based techniques,streams pool-based techniques,label requests,noise-free settings,learning artificial intelligence |
Field | DocType | ISSN |
Training set,Data mining,Semi-supervised learning,Annotation,Active learning,Pattern recognition,Computer science,Artificial intelligence,Machine learning | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 2 | 0.36 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyue Zhao | 1 | 67 | 4.71 |
| Gita Sukthankar | 2 | 538 | 60.40 |
| Rahul Sukthankar | 3 | 6137 | 365.45 |