Quick Hidden Layer Size Tuning in ELM for Classification Problems
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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Brno University of Technology
2024-06-01
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| Series: | Mendel |
| Subjects: | |
| Online Access: | https://mendel-journal.org/index.php/mendel/article/view/299 |
| Summary: | 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.
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| ISSN: | 1803-3814 2571-3701 |