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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Zixing Deng, Jun Coutinho, Eduardo Schuller, Björn |
| Copyright Year | 2015 |
| 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. |
| Starting Page | 115 |
| Ending Page | 126 |
| Page Count | 12 |
| File Format | |
| ISSN | 23299290 |
| e-ISSN | 23299304 |
| DOI | 10.1109/TASLP.2014.2375558 |
| Volume Number | 23 |
| Issue Number | 1 |
| Journal | IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-01-01 |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Acoustics Active learning Cooperative learning Emotion recognition Multi-view learning Semi-supervised learning Supervised learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology |
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