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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Sarutte Atsawaraungsuk
Udon Thani Rajabhat University
Computer Science & IT

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A progressive learning for structural tolerance online sequential extreme learning machine Sarutte Atsawaraungsuk; Wasaya Boonphairote; Kritsanapong Somsuk; Chanwit Suwannapong; Suchart Khummanee
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24564

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.
Co-Authors Chanwit Suwannapong Kritsanapong Somsuk Suchart Khummanee Wasaya Boonphairote