A Class Incremental Extreme Learning Machine for Activity Recognition
文献类型：期刊论文
| 作者 | Zhao, Zhongtang ; Chen, Zhenyu ; Chen, Yiqiang ; Wang, Shuangquan ; Wang, Hongan |
| 刊名 | COGNITIVE COMPUTATION
 |
| 出版日期 | 2014 |
| 卷号 | 6期号:3页码:423-431 |
| 关键词 | Extreme learning machine Incremental learning Activity recognition Mobile device |
| ISSN号 | 1866-9956 |
| 中文摘要 | Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method. |
| 英文摘要 | Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000341593600012 |
| 公开日期 | 2014-12-16 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16826]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Zhao, Zhongtang,Chen, Zhenyu,Chen, Yiqiang,et al. A Class Incremental Extreme Learning Machine for Activity Recognition[J]. COGNITIVE COMPUTATION,2014,6(3):423-431. |
| APA | Zhao, Zhongtang,Chen, Zhenyu,Chen, Yiqiang,Wang, Shuangquan,&Wang, Hongan.(2014).A Class Incremental Extreme Learning Machine for Activity Recognition.COGNITIVE COMPUTATION,6(3),423-431. |
| MLA | Zhao, Zhongtang,et al."A Class Incremental Extreme Learning Machine for Activity Recognition".COGNITIVE COMPUTATION 6.3(2014):423-431. |
入库方式： OAI收割
来源：软件研究所
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