摘要
In this paper, we analyze the convergence of an iterative self-training semi-supervised support vector machine (SVM) algorithm, which is designed for classification in small training data case. This algorithm converges fast and has low computational burden. Its effectiveness is also demonstrated by our data analysis results. Furthermore, we illustrate that this algorithm can be used to significantly reduce training effort and improve adaptability of a brain computer interface (BCI) system, a P300-based speller.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 |
| 页 | I385-I388 |
| DOI | |
| 出版状态 | 已出版 - 2007 |
| 已对外发布 | 是 |
| 活动 | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, 美国 期限: 15 4月 2007 → 20 4月 2007 |
出版系列
| 姓名 | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| 卷 | 1 |
| ISSN（印刷版） | 1520-6149 |
会议
| 会议 | 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 |
|---|---|
| 国家/地区 | 美国 |
| 市 | Honolulu, HI |
| 时期 | 15/04/07 → 20/04/07 |
指纹
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Li, Y., Li, H., Guan, C., & Chin, Z. (2007). A self-training semi-supervised support vector machine algorithm and its applications in brain computer interface. 在 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (页码 I385-I388). 文章 4217097 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; 卷 1). https://doi.org/10.1109/ICASSP.2007.366697