Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Blog Article

This article discusses the development of the online sequential circular extreme learning machine (OS-CELM) and structural tolerance OS-CELM (STOS-CELM).OS-CELM is developed based on the circular extreme learning machine (CELM) to enable sequential learning.It can update a new chunk of data by spending Box Bag less training time to update the chunk than the batch CELM.STOS-CELM is developed based on an idea similar to that of OS-CELM, but with a Householder block exact inverse QR decomposition (QRD) recursive least squares (QRD-RLS) algorithm to Sauce Pans allow sequential learning and mitigate the criticality of deciding the number of hidden nodes.

In addition, our experiments have shown that given the same hidden node setting, STOS-CELM can deliver accuracy comparable to a batch CELM approach and also has higher accuracy than the original online sequential extreme learning machine (OS-ELM) and structural tolerance OS-ELM (STOS-ELM) in classification problems, especially those involving high dimension datasets.