Output Correction of Recurrence-Aware Long-Term Cognitive Network Classifiers
Recurrence-Aware Long-Term Cognitive Network (rLTCN) classifiers have reported comparable performance to mainstream black-box models, including tree ensembles and support vector machines, in tabular pattern classification tasks. These classifiers use a two-step learning algorithm to address issues t...
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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
MDPI AG
2026-06-01
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| Серія: | Big Data and Cognitive Computing |
| Предмети: | |
| Онлайн доступ: | https://www.mdpi.com/2504-2289/10/6/178 |
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