Quick Hidden Layer Size Tuning in ELM for Classification Problems
| Autor: | Audi Albtoush, Manuel Fernandez-Delgado, Haitham Maarouf, Asmaa Jameel Al Nawaiseh |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Mendel, Vol 30, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1803-3814 2571-3701 |
| DOI: | 10.13164/mendel.2024.1.001 |
| Popis: | The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98\% with equal or better generalization ability. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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