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Publication
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| Research Title |
Enhanced Local Receptive Fields based Extreme Learning Machine using Dominant Patterns Selection |
| Date of Distribution |
27 January 2022 |
| Conference |
| Title of the Conference |
The 25th International Computer Science and Engineering Conference (ICSEC) |
| Organiser |
School of Information and Communication Technology, University of Phayao. |
| Conference Place |
Grand Vista Hotel, Chiang Rai |
| Province/State |
Chiang Rai |
| Conference Date |
18 November 2021 |
| To |
20 November 2021 |
| Proceeding Paper |
| Volume |
2021 |
| Issue |
- |
| Page |
161 - 166 |
| Editors/edition/publisher |
IEEE |
| Abstract |
The local receptive fields based ELM (ELM-LRF) is an extended version of ELM. Its hidden nodes are structured through the local connection approach, which demonstrated satisfactory performance in image classification problems. However, ELM-LRF still requires further improvement because extracting images features directly with random initial weights will generate redundancy features that may degrade its performance in some situations. This paper, therefore, presents a new method named the enhanced local receptive fields based ELM using dominant patterns selection (DP-ELM-LRF) to enhance ELM-LRF, which applies novel feature selection in vehicle detection through the selection of dominant patterns of HOGs (DPHOG) for selecting dominant features in the ELM feature space. DP-ELM-LRF evaluated classification performance on GTI and Concrete Crack datasets for binary classification and MNIST, Semeion, and small NORB datasets for multi-classification. Experiment results demonstrated that the DP-ELM-LRF was superior to the ELM-LRF and other comparative methods of multi-classification, whereas binary classification, DP-ELM-LRF, remains comparable with ELM-LRF. |
| Author |
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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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