TELKOMNIKA Telecommunication Computing Electronics and Control
A progressive learning for structural tolerance online sequential extreme learning machine

TELKOMNIKA Telecommunication Computing Electronics and Control
A progressive learning for structural tolerance online sequential extreme learning machine

Extreme learning machine
Progressive learning
Sequential learning
Stable sequential learning

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.

Sarutte Atsawaraungsuk, Wasaya Boonphairote, Kritsanapong Somsuk, Chanwit Suwannapong, Suchart Khummanee

http://telkomnika.uad.ac.id

Mar 25, 2023

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