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Publication
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| Research Title |
A comparative study of pseudo-inverse computing for the extreme learning machine classifier |
| Date of Distribution |
26 October 2011 |
| Conference |
| Title of the Conference |
Data Mining and Intelligent Information Technology Applications (ICMiA), 2011 3rd International COnference on, Macau, 2011 |
| Organiser |
Advanced Instidutde of Convergence IT(ACCIT) |
| Conference Place |
Westin Resort |
| Province/State |
Macau, China |
| Conference Date |
24 May 2012 |
| To |
24 May 2012 |
| Proceeding Paper |
| Volume |
2011 |
| Issue |
0 |
| Page |
40-45 |
| Editors/edition/publisher |
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| Abstract |
Most feed-forward artificial neural network training algorithms for classification problems are based on an iterative steepest descent technique. Their well-known drawback is slow convergence. A fast solution is an Extreme Learning Machine (ELM) computing the Moore-Penrose inverse using SVD. However, the most significant training time is pseudo-inverse computing. Thus, this paper proposes two fast solutions to pseudo-inverse computing based on QR with pivoting and Fast General Inverse algorithms. They are QR-ELM and GENINV-ELM, respectively. The benchmarks are conducted on 5 standard classification problems, i.e., diabetes, satellite images, image segmentation, forest cover type and sensit vehicle (combined) problems. The experimental results clearly showed that both QR-ELM and GENINV-ELM can speed up the training time of ELM and the quality of their solutions can be compared to that of the original ELM. They also show that QR-ELM is more robust than GENINV-ELM. |
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| Peer Review Status |
มีผู้ประเมินอิสระ |
| Level of Conference |
นานาชาติ |
| Type of Proceeding |
Full paper |
| Type of Presentation |
Oral |
| Part of thesis |
true |
| Presentation awarding |
false |
| Attach file |
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| Citation |
0
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Journal Publication
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