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- Title
A progressive learning for structural tolerance online sequential extreme learning machine.
- Authors
Sarutte Atsawaraungsuk; Wasaya Boonphairote; Kritsanapong Somsuk; Chanwit Suwannapong; Suchart Khummanee
- Abstract
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
- Subjects
SEQUENTIAL learning; MACHINE learning; ONLINE education
- Publication
Telkomnika, 2023, Vol 21, Issue 5, p1039
- ISSN
1693-6930
- Publication type
Article
- DOI
10.12928/TELKOMNIKA.v21i5.24564