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- W4386763635 abstract "The Event Related Potential (ERP) technique provides a powerful way of exploring brain and neuroscience. However, ERP data are susceptible to different noises and artifacts some of which are impossible to be filtered out. In this case, it is necessary to find a strategy to tolerate these noises, ensuring accuracy even lots of noises showing up. One possible strategy is the restart learning rate schedule (restart schedule) which was developed to deal with the multi-modal optimization problems. In order to verify restart schedule’s effectiveness in improving the accuracy of training ERP data, restart schedule was applied to train different ERP datasets and compared to the other schedules in this paper. The experimental results show that the restart schedule performs the best compared to the other schedules." @default.
- W4386763635 created "2023-09-16" @default.
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- W4386763635 date "2023-04-21" @default.
- W4386763635 modified "2023-09-26" @default.
- W4386763635 title "Training Accuracy Improvement for ERP Datasets by Employing Restart Learning Strategy" @default.
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- W4386763635 doi "https://doi.org/10.1109/icbcb57893.2023.10246586" @default.
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