Multicriteria Learning: Combining Minimum Mean Square Error, Minimum Error Entropy, and Maximum Correntropy in the Presence of Gaussian and Non‐Gaussian Noises

Understanding natural phenomena relies heavily on learning criteria aimed at minimizing model errors and enhancing fidelity to real‐world conditions. This study investigates a linear combination of three criteria: minimum mean square error (MMSE), minimum error entropy (MEE), and maximum correntropy criterion (MCC), using a gradient descent algorithm. We explore three data scenarios: noise‐free, Gaussian noise, and non‐Gaussian noise. Incorporating feedback, we extend our analysis to encompass additional noise models such as Laplacian noise and Poisson noise. Our findings reveal that leveraging the MEE criterion in isolation effectively identifies the model’s target vector, showcasing its resilience across diverse noise conditions.