An efficient running quantile estimation technique alongside correntropy for outlier rejection in online regression
In this paper, online linear regression in the presence of non-Gaussian noise is addressed.
In such environments, there are outliers in error samples (error between system output and …
In such environments, there are outliers in error samples (error between system output and …
Mitigating outlier effect in online regression: An efficient usage of error correntropy criterion
In this paper, a modified version of maximum correntropy criterion (MCC) with application in
online regression (or adaptive filtering) is proposed. It is well known that information …
online regression (or adaptive filtering) is proposed. It is well known that information …
Challenging the Deployment of Fiducial Points in Minimum Error Entropy
In this paper, robust linear adaptive filtering in presence of non-Gaussian noise is
addressed. More precisely, the well-known algorithm for robust adaptive learning called …
addressed. More precisely, the well-known algorithm for robust adaptive learning called …
Trimmed Minimum Error Entropy for Robust Online Regression
In this paper, online linear regression in environ-ments corrupted by non-Gaussian noise is
addressed. In such environments, the error between the system output and the label also …
addressed. In such environments, the error between the system output and the label also …
[图书][B] Robust Online Learning Enabled by Information Theory
S Bahrami - 2021 - search.proquest.com
In this thesis, we incorporate information theory into statistical signal processing and
machine learning in order to achieve robust learning in presence of outliers (or generally in …
machine learning in order to achieve robust learning in presence of outliers (or generally in …