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Found 6 papers, 0 papers with code
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging
Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task.
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity.
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination
In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with adaptive sparseness.
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding
To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm.
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
The aim of this study is to propose a new robust implementation for PLSR.
Restricted Minimum Error Entropy Criterion for Robust Classification
The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning.
