Regularization Penalty

Regularization penalties are techniques used in machine learning to prevent overfitting and improve model generalization, particularly when dealing with limited or noisy data, or when facing distributional shifts between training and testing data. Current research focuses on developing novel penalty functions tailored to specific challenges, such as handling augmented classes (classes unseen during training), incorporating data quality variance, and exploiting inherent data structures like ordinality in ratings or temporal dependencies in time series data. These advancements are crucial for improving the robustness and reliability of machine learning models across diverse applications, ranging from medical imaging and recommender systems to electroencephalogram (EEG) analysis and reinforcement learning.