Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of human annotation. The core underlying idea of Cooperative Learning is to share the labeling work between human and machine efficiently in such a way that instances predicted with insufficient confidence value are subject to human labeling, and those with high confidence values are machine labeled. We conducted various test runs on two emotion recognition tasks with a variable number of initial supervised training instances and two different feature sets. The results show that Cooperative Learning consistently outperforms individual Active and Semi-Supervised Learning techniques in all test cases. In particular, we show that our method based on the combination of Active Learning and Co-Training leads to the same performance of a model trained on the whole training set, but using 75% fewer labeled instances. Therefore, our method efficiently and robustly reduces the need for human annotations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TASLP.2014.2375558 | IEEE/ACM Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
supervised learning,semisupervised learning technique,speech recognition,acoustics,cooperative learning,learning (artificial intelligence),semi-supervised learning,human annotation effect,active learning,emotion recognition,active learning technique,multi-view learning,unlabeled data exploitation,data models,semi supervised learning,labeling,speech processing,speech | Semi-supervised learning,Instance-based learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Online machine learning,Multi-task learning,Active learning,Stability (learning theory),Pattern recognition,Speech recognition,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 1 | 2329-9290 |
Citations | PageRank | References |
21 | 0.70 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zixing Zhang | 1 | 397 | 31.73 |
| Eduardo Coutinho | 2 | 48 | 6.42 |
| Jun Deng | 3 | 278 | 18.59 |
| Björn Schuller | 4 | 6749 | 463.50 |