| Title of Article |
Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine |
| Date of Acceptance |
31 July 2017 |
| Journal |
| Title of Journal |
International Journal of Technology |
| Standard |
SCOPUS |
| Institute of Journal |
IJTech secretariat, Gd. Engineering Center Lt.2, Faculty of Engineering, Universitas Indonesia Depok 16424, Indonesia. |
| ISBN/ISSN |
20869614 |
| Volume |
4 |
| Issue |
8 |
| Month |
8 |
| Year of Publication |
2017 |
| Page |
601-610 |
| Abstract |
This article discusses the development of the online sequential circular extreme learning machine (OS-CELM) and structural tolerance OS-CELM (STOS-CELM). OS-CELM is developed based on the circular extreme learning machine (CELM) to enable sequential learning. It can update a new chunk of data by spending less training time to update the chunk than the batch CELM. STOS-CELM is developed based on an idea similar to that of OS-CELM, but with a Householder block exact inverse QR decomposition (QRD) recursive least squares (QRD-RLS) algorithm to allow sequential learning and mitigate the criticality of deciding the number of hidden nodes. In addition, our experiments have shown that given the same hidden node setting, STOS-CELM can deliver accuracy comparable to a batch CELM approach and also has higher accuracy than the original online sequential extreme learning machine (OS-ELM) and structural tolerance OS-ELM (STOS-ELM) in classification problems, especially those involving high dimension datasets. |
| Keyword |
Circular extreme learning machine; Extreme learning machine; Householder block exact QRD recursive least squares algorithm; Online sequential extreme learning machine |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Publication
Journal Publication
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