Voting base online sequential extreme learning machine for multi-class classification
In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential...
Saved in:
| Main Authors: | , , |
|---|---|
| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2013
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/102945 http://hdl.handle.net/10220/16887 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In this paper, we propose a voting based online sequential extreme learning machine (VOS-ELM) for single hidden layer feedforward networks (SLFNs) to perform the online sequential multi-class classification. Utilizing the recent voting based extreme learning machine (V-ELM) and the online sequential extreme learning machine (OS-ELM), the newly developed VOS-ELM is able to classify online sequences by learning data one-by-one or chunk-by-chunk with fixed or varying chunk size and to reach a higher classification accuracy than the original OS-ELM. Simulations on several real world classification datasets show that VOS-ELM outperforms OS-ELM as well as several state-of-the-art online sequential algorithms. |
|---|