Name
Abstract | ||
|---|---|---|
We propose a recursive singular value decomposition (SVD)-based fuzzy extreme learning machine (RSVD-F-ELM) for the online learning in classification or regression analysis. By adopting the same architecture and operation as fuzzy extreme learning machine (F-ELM), which is originally designed for the batch learning, and replacing the Moore-Penrose generalized inverse in F-ELM with a recursive SVD-based least squares estimator for optimizing the output weights recursively, RSVD-F-ELM is applicable for the online learning. Compared with the other online learning approach, namely online sequential fuzzy extreme learning machine (OS-F-ELM), experimental results have revealed that RSVD-F-ELM generates the larger accuracy rates in classification analysis and the smaller mean squared errors in regression analysis. Moreover, the learning stability of RSVD-F-ELM is much better. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICInfA.2017.8078953 | 2017 IEEE International Conference on Information and Automation (ICIA) |
Keywords | Field | DocType |
classification,regression,singular value decomposition,fuzzy inference system,extreme learning machine,least squares estimator,online learning,batch learning | Online machine learning,Semi-supervised learning,Instance-based learning,Stability (learning theory),Pattern recognition,Active learning (machine learning),Extreme learning machine,Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory | Conference |
ISBN | Citations | PageRank |
978-1-5386-3155-3 | 0 | 0.34 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen-Sen Ouyang | 1 | 157 | 17.15 |
| Yu-Yuan Cheng | 2 | 0 | 0.34 |
| Tzu-Chin Kao | 3 | 0 | 0.34 |
| Shing-Tai Pan | 4 | 104 | 13.46 |
| Chih-Hung Wu | 5 | 153 | 23.29 |
| Shie-Jue Lee | 6 | 48 | 5.11 |